US20050027691A1 - System and method for providing a user interface with search query broadening - Google Patents
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
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Definitions
- the present invention relates in general to query processing and, in particular, to a system and method for providing a user interface with search query broadening.
- Web content retrieval and presentation applications typically in the form of Web browsers.
- the Web browsers send request messages for Web content to centralized Web servers, which function as data storage and retrieval repositories.
- the Web servers parse the request messages and return the requested Web content in response messages.
- Search engines have evolved in tempo with the increased usage of the Web to enable users to find and retrieve relevant Web content in an efficient and timely manner. As the amount and types of Web content has increased, the sophistication and accuracy of search engines has likewise improved. Generally, search engines strive to provide the highest quality results in response to a search query. However, determining quality is difficult, as the relevance of retrieved Web content is inherently subjective and dependent upon the interests, knowledge and attitudes of the user.
- a typical search query scenario begins with either a natural language question or individual terms, often in the form of keywords, being submitted to a search engine.
- the search engine executes a search against a data repository describing information characteristics of potentially retrievable Web content and identifies the candidate Web pages. Searches can often return thousands or even millions of results, so most search engines typically rank or score only a subset of the most promising results.
- the top Web pages are then presented to the user, usually in the form of Web content titles, hyperlinks, and other descriptive information, such as snippets of text taken from the Web pages.
- a given search query can often be expressed in multiple forms based on the individual terms, which constitute the search query. For instance, a particular term may have synonyms, alternate spellings, share a common root form, or have a semantic meaning similar to other words. Likewise, a particular term may share a conceptual meaning with other related words. Moreover, an individual term could be part of a compound term, which, when combined with one or more other terms, may also have multiple forms.
- search result quality can affect search result quality.
- a user may simply fail to realize the scope of the search and could provide a set of individual terms that inadvertently limit the search to a portion of potentially responsive results.
- the user could possess a limited vocabulary due to, for instance, an unfamiliarity with the subject matter of the search, and consequently poorly frame or even mischaracterize the query.
- the user might misunderstand the techniques employed by a particular search engine and provide terms that are ill-suited to the search engine.
- search characteristics may be used, such as synonyms, alternate spellings, terms that share a common root form, or have a semantic meaning similar to other words, as well as search characteristics that share a conceptual meaning with other related words.
- the broadened search scope can result in increased search quality.
- An embodiment provides a system and method for providing a user interface with search query broadening.
- a query defining a search executable on potentially retrievable information is accepted.
- the query is parsed.
- a broadened query is provided. At least one of the broadened query and results of a search executed on the broadened query is presented.
- FIG. 1 is a block diagram showing a system for providing a user interface with search query broadening, in accordance with the present invention.
- FIG. 2 is a block diagram showing a computer system for use in the system of FIG. 1 .
- FIGS. 3A-3B are diagrams showing, by way of example, a search query presented relative to broadened search characteristics determined in accordance with the present invention.
- FIG. 4 is a screen shot showing, by way of example, a Web page containing search results retrieved through broadened search characteristics.
- FIG. 5 is a flow diagram showing a method for providing a user interface with search query broadening, in accordance with the present invention.
- FIG. 6 is a flow diagram showing a routine for broadening a query for use in the method of FIG. 5 .
- FIG. 7 is a flow diagram showing a routine for presenting a broadened query for use in the method of FIG. 5 .
- FIG. 1 is a block diagram showing a system 10 for providing a user interface with search query broadening, in accordance with the present invention.
- a plurality of individual clients 12 are communicatively interfaced to a server 11 via an internetwork 13 , such as the Internet, or other form of communications network, as would be recognized by one skilled in the art.
- the individual clients 12 are operated by users 19 who transact requests for Web content and other operations through their respective client 12 .
- each client 12 can be any form of computing platform connectable to a network, such as the internetwork 13 , and capable of interacting with application programs.
- a network such as the internetwork 13
- Exemplary examples of individual clients include, without limitation, personal computers, digital assistances, “smart” cellular telephones and pagers, lightweight clients, workstations, “dumb” terminals interfaced to an application server, and various arrangements and configurations thereof, as would be recognized by one skilled in the art.
- the internetwork 13 includes various topologies, configurations, and arrangements of network interconnectivity components arranged to interoperatively couple with enterprise, wide area and local area networks and include, without limitation, conventionally wired, wireless, satellite, optical, and equivalent network technologies, as would be recognized by one skilled in the art.
- each client 12 executes a Web browser 18 (“Web browser”), which preferably implements a graphical user interface and through which search queries are sent to a Web server 20 executing on the server 11 , as further described below with reference to FIG. 2 .
- Web browser Web browser
- Each search query describes or identifies information, generally in the form of Web content, which is potentially retrievable via the Web server 20 .
- the search query provides search characteristics, typically expressed as individual terms, such as keywords and the like, and attributes, such as language, character encoding and so forth, which enables a search engine 21 , also executing on the server 11 , to identify and send back Web pages.
- Other styles, forms or definitions of search queries and characteristics are feasible, as would be recognized by one skilled in the art.
- the Web pages are sent back to the Web browser 18 for presentation, usually in the form of Web content titles, hyperlinks, and other descriptive information, such as snippets of text taken from the Web pages.
- the user can view or access the Web pages on the graphical user interface and can input selections and responses in the form of typed text, clicks, or both.
- the server 11 maintains an attached storage device 15 in which Web content 22 is maintained.
- the Web content 22 could also be maintained remotely on other Web servers (not shown) interconnected either directly or indirectly via the internetwork 13 and which are preferably accessible by each client 12 .
- the search engine 21 preferably identifies the Web content 22 best matching the search characteristics to provide high quality Web pages, such as described in S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Search Engine” (1998) and in U.S. Pat. No. 6,285,999, issued Sep. 4, 2001 to Page, the disclosures of which are incorporated by reference.
- the search engine 21 operates on information characteristics describing potentially retrievable Web content, as further described below with reference to FIG. 2 .
- the functionality provided by the server 20 including the Web server 20 and search engine 21 , could be provided by a loosely- or tightly-coupled distributed or parallelized computing configuration, in addition to a uniprocessing environment.
- the individual computer systems include general purpose, programmed digital computing devices consisting of a central processing unit (processors 13 and 16 , respectively), random access memory (memories 14 and 17 , respectively), non-volatile secondary storage 15 , such as a hard drive or CD ROM drive, network or wireless interfaces, and peripheral devices, including user interfacing means, such as a keyboard and display.
- Program code including software programs, and data is loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage.
- the Web browser 18 is an HTTP-compatible Web browser, such as the Internet Explorer, licensed by Microsoft Corporation, Redmond, Wash.; Navigator, licensed by Netscape Corporation, Mountain View, Calif.; or a Mozilla or JavaScript enabled browser, as are known in the art.
- FIG. 2 is a block diagram showing a computer system 30 for use in the system 10 of FIG. 1 .
- the computer system 30 includes a processor 31 and visual display 32 , such as a computer monitor or liquid crystal diode (LCD) display, as are known in the art.
- the computer system 30 executes a Web browser 18 (shown in FIG. 1 ), which preferably implements a graphical user interface 37 .
- Visual Web content is output within a display area defined on the graphical user interface 37 while user inputs are generally input both within the display area and within specified user input regions.
- Textual user inputs are received via a keyboard 33 .
- Linear, non-textual inputs are received via an optional pointing device 34 , such as a mouse, trackball, track pad, or arrow keys.
- search queries are entered as user inputs and each search query is dynamically broadened, as further described below with reference to FIG. 6 .
- the broadened search terms can be presented, generally as visual Web content, as further described below with reference to FIG. 7 , and executed.
- Other forms of computer components, including processor 31 , visual display 32 , and input and output devices could be used, as would be recognized by one skilled in the art.
- FIGS. 3A-3B are diagrams showing, by way of example, a search query 40 presented relative to broadened search characteristics, such as query terms 43 , 44 , determined in accordance with the present invention.
- the example search query 40 constitutes a list of the individual terms 41 consisting of “car,” “repair,” and “help.”
- One or more of the individual terms 41 are broadened to enable the search engine 21 generate search results using a query framed more loosely or broadly than with the original unbroadened terms 41 .
- Broadening refers to modifying the scope of the search query 40 , such that the search results reflect an increased breadth, rather than a narrowing, limiting, or otherwise restricting of the search scope. Broadening can include modifying, replacing, supplementing, removing, or otherwise restating one or more search characteristics, such as terms 41 , but need not be so limited. Search characteristics include query terms, concepts and other forms of information specified in or derivable from a query to help identify the content sought. Broadening can also include rewriting or modifying the query 40 either in whole or in part. As an example, broadening can include providing synonyms, alternate spellings, common root forms, or terms having a similar semantic meaning or terms sharing a conceptual meaning.
- broadening can include altering the query scope through query modification, such as by excluding a broadened term in an initial search and evaluating the search results relative to the excluded term using categorical or clustered distinctions. Broadening can also include, for example, determining a semantic or conceptual meaning of a query, or one or more search characteristics in a query, and broadening the query based on related semantic or conceptual meanings; such broadening may, but need not, be performed using related search characteristics. Consequently, broadening encompasses analyzing the query 40 and introducing changes to effect broader search result scope and increased search quality.
- one or more words related to at least one of the original terms 41 could be disjunctively added to the original query 40 .
- the search engine 21 would then execute the expanded query using the broadened list of terms.
- the individual term 41 of “car” could be supplemented with “vehicle” and the search engine 21 would execute a search on a query consisting of the terms “car,” “vehicle,” “repair,” and “help.”
- the search engine 21 could initially execute a query, which excludes an original term 41 , and map the search results into categories of related terms associated with the excluded original term 41 .
- the individual term 41 of “help” could be excluded and the search engine 21 would execute a search on a query consisting only of the terms “car” and “repair.”
- the search engine 21 would then map the resultant search results into specific categories of related terms associated with the term of “help.”
- the search engine 21 could form clusters of search results, rather than mapping the search results into categories. Clusters group search results at a conceptual level, whereas categories group at a literal level of related terms
- the individual term 41 of “car” has been broadened to include the list of broadened terms 43 consisting of “cars,” “autos,” and “auto.”
- broadened terms 43 consisting of “cars,” “autos,” and “auto.”
- Each of the sample broadened terms have been selected from a set of words having a synonymous, alternate spelling, common root, or similar semantic meaning, although other selections of broadened terms could be used in addition to or in combination with the foregoing words set, which can be selected and combined in various styles and arrangements, as would be recognized by one skilled in the art.
- the individual term 41 of “help” has been broadened to include the list of broadened terms 44 consisting of “guide,” “tips,” “tutorial,” and “problem.”
- broadened terms 44 consisting of “guide,” “tips,” “tutorial,” and “problem.”
- Each of the sample broadened terms have been selected from a set of words having a related conceptual meaning, although other selections of broadened terms could be used in addition to or in combination with the foregoing words set, which can be selected and combined in various styles and arrangements, as would be recognized by one skilled in the art.
- each individual term 41 being broadened could be provided as a hyperlink, either with or without the broadened terms list 43 , 44 .
- each broadened term 43 , 44 forms a part of the broadened query upon selection by the user.
- each selected term 41 is broadened upon selection by the user.
- individual terms 41 are logically grouped with one or more other individual terms 41 to form a compound term and a set of broadened terms is determined for the compound term.
- the term 41 of “hot” followed by the term of “dog” could be logically grouped to form the compound term “hot dog” and a list of broadened terms could include the term 41 of “hamburger,” “wiener,” and “sausage.”
- Other forms of identifying and combining individual terms 41 to logically form compound and complex terms are possible, as would be recognized by one skilled in the art.
- Both broadened terms lists 43 , 44 are presented as a static list, although other forms of presentation, including a menu of selectable terms, a list of selectable terms, a set of checkboxes, and a set of hyperlinks corresponding to each broadened term, either with or without a broadened terms list, could be used, as would be recognized by one skilled in the art.
- a user interface allowing selection of one or more choices may also be employed to restrict or focus queries, as opposed to broadening queries. For example, from a query containing the term “vegetarian,” the choices “ovo lacto,” “lacto” and “vegan” may be generated, and a user may be allowed to select one or more of the choices to focus an original query.
- a “select all” option 42 is provided supplemental to the individual terms list 41 . Selection of the “select all” option 42 triggers the selection of each of the broadened terms lists 43 , 44 , although other forms of full or partial broadened terms selection are possible, as would be recognized by one skilled in the art.
- FIG. 4 is a screen shot showing, by way of example, a Web page 50 containing search results 52 retrieved through broadened search characteristics.
- the search query constitutes a list of individual terms 41 consisting of “car,” “repair,” and “help.”
- the example search results 52 match the list of terms consisting of “auto,” “guide,” and “problems,” reflecting a broadening of the terms 41 of “car” and “help.”
- An operator ‘ ⁇ ’ is prepended to the terms “car” and “help” to expressly request query broadening with respect to the indicated terms.
- a delimiter such as the ‘/’ character, can be provided with the operator ‘ ⁇ ’ to signal an ordinary meaning with respect to the operator.
- the term “/ ⁇ car” would signal that the term should be treated by the search engine 21 as consisting of “/ ⁇ car” without query broadening.
- every search characteristic, including term 41 , in a search query 40 could be automatically broadened by including at least one occurrence of the operator within the search query 40 .
- search query broadening is instead requested through the use of hyperlinks associated with one or more individual search characteristics, including terms 41 .
- search characteristic would be broadened upon selection of the associated hyperlink by the user.
- Other forms of operators and delimiters are possible, as would be recognized by one skilled in the art.
- the use of the operator ‘ ⁇ ’ includes an assignable strength, which could be, for example, indicated through repetition of the operator or through the use of alternative operators.
- an assignable strength which could be, for example, indicated through repetition of the operator or through the use of alternative operators.
- the term 41 of “ ⁇ ⁇ help” appearing with two occurrences of the operator ‘ ⁇ ’ would indicate that the term 41 should be broadened further than indicated by the occurrence of a single operator ‘ ⁇ .’
- the types of further broadening include broadening based on categories or clusters of related search characteristics, as well as other forms of broadening, as are known in the art.
- other types and forms of operators to indicate an assignable strength are possible, as would be recognized by one skilled in the art.
- FIG. 5 is a flow diagram showing a method 40 for providing a user interface with search query broadening, in accordance with the present invention.
- the method 40 is described as a sequence of process operations or steps, which can be executed, for instance, by a search engine 21 (shown in FIG. 1 ).
- a search query 40 is accepted from a user 19 and parsed into individual search characteristics (block 61 ).
- the search query 40 is broadened (block 62 ), as further described below with reference to FIG. 6 .
- one or more of the search characteristics in the search query 40 could be broadened.
- the broadened search query is then presented to the user 19 (block 64 ), as further described below with reference to FIG. 7 .
- the query is executed (block 65 ) to identify Web content 22 best matching the search characteristics, such as described in S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Search Engine” (1998) and in U.S. Pat. No. 6,285,999, issued Sep.
- search results 52 are presented via the browser 18 (block 66 ). Typically, only a part of the search results 52 need be presented since the full set of search results 52 can exceed available presentation space on the browser 18 . The method then terminates.
- FIG. 6 is a flow diagram showing a routine 70 for broadening a query for use in the method of FIG. 5 .
- the purpose of this routine is to identify, broaden and provide a broadened search query 40 , such as by broadening individual search characteristics occurring in a search query 40 , including query terms 41 .
- Each search characteristic in the search query 40 is iteratively processed (blocks 71 - 77 ) as follows, although other forms of non-iterative processing are possible, as would be recognized by one skilled in the art.
- Each search characteristic is evaluated to determine if an operator expressly requesting query broadening, such as an operator ‘ ⁇ ,’ is included (block 72 ). If no operator is included, the search characteristic is not broadened and the next search characteristic is processed (block 77 ). If an operator is included, the search characteristic is further evaluated to determine if a delimiter signaling ordinary meaning with respect to the operator is included (block 73 ). If a delimiter is included, the search characteristic is not broadened and the next search characteristic is processed (block 77 ).
- the search characteristic is evaluated to determine if the search characteristic should be logically grouped with one or more other search characteristics in the search query 40 to form a compound search characteristic (block 74 ). If the search characteristic should not be logically grouped, broadening search characteristics are provided for just the individual search characteristic (block 75 ). Otherwise, if the search characteristic should be logically grouped, broadening search characteristics are provided for the compound search characteristic (block 76 ).
- the set of broadening search characteristics are generated by receiving one or more example search characteristics corresponding to each search characteristic to be broadened. Weights are assigned to each example search characteristic and a list of broadened search characteristics is formed based on the example search characteristics and the weights assigned to each example search characteristic, such as described in related U.S. patent application Ser. No. 10/425,819, filed Apr. 30, 2003, pending, the disclosure of which is incorporated by reference. Other approaches to generating the broadening search characteristics set are possible, as would be recognized by one skilled in the art.
- search query broadening (blocks 75 and 76 )
- the next search characteristic is processed (block 77 ), after which the routine returns.
- FIG. 7 is a flow diagram showing a routine 60 for presenting a broadened query for use in the method of FIG. 5 .
- the purpose of this routine is to flexibly present the set of broadened search characteristics to the user relative to the set of search characteristics from the original search query 40 .
- Each search characteristic in the search query is iteratively processed (blocks 81 - 94 ) as follows, although other forms of non-iterative processing are possible, as would be recognized by one skilled in the art.
- a hyperlink presentation form is desired (block 82 )
- the search characteristic is presented as a hyperlink (block 83 ), either with or without a broadened search characteristics list.
- list form each broadened search characteristic forms a part of the broadened query upon selection by the user.
- non-list form each selected search characteristic is broadened upon selection by the user.
- a static list presentation form is desired (block 84 )
- the search characteristic is presented as part of a static list (block 84 ).
- a menu presentation form is desired (block 86 )
- the search characteristic is presented as part of a menu of selectable search characteristics (block 84 ) and the search characteristic forms a part of the broadened query upon selection by the user.
- a selectable list presentation form is desired (block 88 )
- the search characteristic is presented as part of a list of selectable search characteristics (block 89 ) and the search characteristic forms a part of the broadened query upon selection by the user.
- a checkbox list presentation form is desired (block 90 )
- each broadened search characteristic is presented as part of a list of checkbox selectable search characteristics (block 92 ) and the search characteristic forms a part of the broadened query upon selection by the user.
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Abstract
Description
- The present invention relates in general to query processing and, in particular, to a system and method for providing a user interface with search query broadening.
- Although the Internet traces back to the late 1960s, the widespread availability and acceptance of personal computing and internetworking have resulted in the explosive growth and unprecedented advances in information sharing technologies. In particular, the Worldwide Web (“Web”) has revolutionized accessibility to untold volumes of information in stored electronic form to a worldwide audience, including written, spoken (audio) and visual (imagery and video) information, both in archived and real-time formats. In short, the Web has provided desktop access to every connected user to a virtually unlimited library of information in almost every language worldwide.
- Information exchange on the Web operates under a client-server model. Individual clients execute Web content retrieval and presentation applications, typically in the form of Web browsers. The Web browsers send request messages for Web content to centralized Web servers, which function as data storage and retrieval repositories. The Web servers parse the request messages and return the requested Web content in response messages.
- Search engines have evolved in tempo with the increased usage of the Web to enable users to find and retrieve relevant Web content in an efficient and timely manner. As the amount and types of Web content has increased, the sophistication and accuracy of search engines has likewise improved. Generally, search engines strive to provide the highest quality results in response to a search query. However, determining quality is difficult, as the relevance of retrieved Web content is inherently subjective and dependent upon the interests, knowledge and attitudes of the user.
- Existing methods used by search engines are based on matching search query terms to terms indexed from Web pages. More advanced methods determine the importance of retrieved Web content using, for example, a hyperlink structure-based analysis, such as described in S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Search Engine,” (1998) and in U.S. Pat. No. 6,285,999, issued Sep. 4, 2001 to Page, the disclosures of which are incorporated by reference.
- A typical search query scenario begins with either a natural language question or individual terms, often in the form of keywords, being submitted to a search engine. The search engine executes a search against a data repository describing information characteristics of potentially retrievable Web content and identifies the candidate Web pages. Searches can often return thousands or even millions of results, so most search engines typically rank or score only a subset of the most promising results. The top Web pages are then presented to the user, usually in the form of Web content titles, hyperlinks, and other descriptive information, such as snippets of text taken from the Web pages.
- Providing quality search results is complicated by several factors. First, a given search query can often be expressed in multiple forms based on the individual terms, which constitute the search query. For instance, a particular term may have synonyms, alternate spellings, share a common root form, or have a semantic meaning similar to other words. Likewise, a particular term may share a conceptual meaning with other related words. Moreover, an individual term could be part of a compound term, which, when combined with one or more other terms, may also have multiple forms.
- In addition, the choice of terms selected by a user submitting a search query can affect search result quality. A user may simply fail to realize the scope of the search and could provide a set of individual terms that inadvertently limit the search to a portion of potentially responsive results. Similarly, the user could possess a limited vocabulary due to, for instance, an unfamiliarity with the subject matter of the search, and consequently poorly frame or even mischaracterize the query. Finally, the user might misunderstand the techniques employed by a particular search engine and provide terms that are ill-suited to the search engine.
- Accordingly, there is a need for an approach to broadening a search query. Broadening search characteristics may be used, such as synonyms, alternate spellings, terms that share a common root form, or have a semantic meaning similar to other words, as well as search characteristics that share a conceptual meaning with other related words. The broadened search scope can result in increased search quality.
- There is a further need for an approach to providing a user interface presenting broadened search queries. In one example, individual search characteristics occurring as part of a compound term would be identified and considered when providing one or more broadening search characteristics. Such broadened search characteristics may be flexibly presented to the user in multiple display formats.
- An embodiment provides a system and method for providing a user interface with search query broadening. A query defining a search executable on potentially retrievable information is accepted. The query is parsed. A broadened query is provided. At least one of the broadened query and results of a search executed on the broadened query is presented.
- Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein are described embodiments of the invention by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.
- The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with the color drawings will be provided by the Office upon request and payment of the necessary fee.
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FIG. 1 is a block diagram showing a system for providing a user interface with search query broadening, in accordance with the present invention. -
FIG. 2 is a block diagram showing a computer system for use in the system ofFIG. 1 . -
FIGS. 3A-3B are diagrams showing, by way of example, a search query presented relative to broadened search characteristics determined in accordance with the present invention. -
FIG. 4 is a screen shot showing, by way of example, a Web page containing search results retrieved through broadened search characteristics. -
FIG. 5 is a flow diagram showing a method for providing a user interface with search query broadening, in accordance with the present invention. -
FIG. 6 is a flow diagram showing a routine for broadening a query for use in the method ofFIG. 5 . -
FIG. 7 is a flow diagram showing a routine for presenting a broadened query for use in the method ofFIG. 5 . - System Overview
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FIG. 1 is a block diagram showing asystem 10 for providing a user interface with search query broadening, in accordance with the present invention. A plurality ofindividual clients 12 are communicatively interfaced to aserver 11 via aninternetwork 13, such as the Internet, or other form of communications network, as would be recognized by one skilled in the art. Theindividual clients 12 are operated byusers 19 who transact requests for Web content and other operations through theirrespective client 12. - In general, each
client 12 can be any form of computing platform connectable to a network, such as theinternetwork 13, and capable of interacting with application programs. Exemplary examples of individual clients include, without limitation, personal computers, digital assistances, “smart” cellular telephones and pagers, lightweight clients, workstations, “dumb” terminals interfaced to an application server, and various arrangements and configurations thereof, as would be recognized by one skilled in the art. Theinternetwork 13 includes various topologies, configurations, and arrangements of network interconnectivity components arranged to interoperatively couple with enterprise, wide area and local area networks and include, without limitation, conventionally wired, wireless, satellite, optical, and equivalent network technologies, as would be recognized by one skilled in the art. - For Web content exchange and, in particular, to transact searches, each
client 12 executes a Web browser 18 (“Web browser”), which preferably implements a graphical user interface and through which search queries are sent to a Web server 20 executing on theserver 11, as further described below with reference toFIG. 2 . Each search query describes or identifies information, generally in the form of Web content, which is potentially retrievable via the Web server 20. The search query provides search characteristics, typically expressed as individual terms, such as keywords and the like, and attributes, such as language, character encoding and so forth, which enables asearch engine 21, also executing on theserver 11, to identify and send back Web pages. Other styles, forms or definitions of search queries and characteristics are feasible, as would be recognized by one skilled in the art. - The Web pages are sent back to the
Web browser 18 for presentation, usually in the form of Web content titles, hyperlinks, and other descriptive information, such as snippets of text taken from the Web pages. The user can view or access the Web pages on the graphical user interface and can input selections and responses in the form of typed text, clicks, or both. Theserver 11 maintains an attachedstorage device 15 in whichWeb content 22 is maintained. TheWeb content 22 could also be maintained remotely on other Web servers (not shown) interconnected either directly or indirectly via theinternetwork 13 and which are preferably accessible by eachclient 12. - The
search engine 21 preferably identifies theWeb content 22 best matching the search characteristics to provide high quality Web pages, such as described in S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Search Engine” (1998) and in U.S. Pat. No. 6,285,999, issued Sep. 4, 2001 to Page, the disclosures of which are incorporated by reference. In identifyingmatching Web content 22, thesearch engine 21 operates on information characteristics describing potentially retrievable Web content, as further described below with reference toFIG. 2 . Note the functionality provided by the server 20, including the Web server 20 andsearch engine 21, could be provided by a loosely- or tightly-coupled distributed or parallelized computing configuration, in addition to a uniprocessing environment. - The individual computer systems, including
server 11 andclients 12, include general purpose, programmed digital computing devices consisting of a central processing unit (processors memories secondary storage 15, such as a hard drive or CD ROM drive, network or wireless interfaces, and peripheral devices, including user interfacing means, such as a keyboard and display. Program code, including software programs, and data is loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage. TheWeb browser 18 is an HTTP-compatible Web browser, such as the Internet Explorer, licensed by Microsoft Corporation, Redmond, Wash.; Navigator, licensed by Netscape Corporation, Mountain View, Calif.; or a Mozilla or JavaScript enabled browser, as are known in the art. - Computer System Components
-
FIG. 2 is a block diagram showing acomputer system 30 for use in thesystem 10 ofFIG. 1 . Thecomputer system 30 includes aprocessor 31 andvisual display 32, such as a computer monitor or liquid crystal diode (LCD) display, as are known in the art. Thecomputer system 30 executes a Web browser 18 (shown inFIG. 1 ), which preferably implements agraphical user interface 37. Visual Web content is output within a display area defined on thegraphical user interface 37 while user inputs are generally input both within the display area and within specified user input regions. Textual user inputs are received via akeyboard 33. Linear, non-textual inputs are received via anoptional pointing device 34, such as a mouse, trackball, track pad, or arrow keys. Similarly, voice- and sound-based inputs are received via amicrophone 35. Visual outputs are displayed via thegraphical user interface 37 on thevisual display 32, while audio outputs are played on thespeakers 36. In particular, search queries are entered as user inputs and each search query is dynamically broadened, as further described below with reference toFIG. 6 . The broadened search terms can be presented, generally as visual Web content, as further described below with reference toFIG. 7 , and executed. Other forms of computer components, includingprocessor 31,visual display 32, and input and output devices could be used, as would be recognized by one skilled in the art. - Sample Screen Shot
-
FIGS. 3A-3B are diagrams showing, by way of example, asearch query 40 presented relative to broadened search characteristics, such asquery terms example search query 40 constitutes a list of theindividual terms 41 consisting of “car,” “repair,” and “help.” One or more of theindividual terms 41 are broadened to enable thesearch engine 21 generate search results using a query framed more loosely or broadly than with the originalunbroadened terms 41. - “Broadening” refers to modifying the scope of the
search query 40, such that the search results reflect an increased breadth, rather than a narrowing, limiting, or otherwise restricting of the search scope. Broadening can include modifying, replacing, supplementing, removing, or otherwise restating one or more search characteristics, such asterms 41, but need not be so limited. Search characteristics include query terms, concepts and other forms of information specified in or derivable from a query to help identify the content sought. Broadening can also include rewriting or modifying thequery 40 either in whole or in part. As an example, broadening can include providing synonyms, alternate spellings, common root forms, or terms having a similar semantic meaning or terms sharing a conceptual meaning. In addition, broadening can include altering the query scope through query modification, such as by excluding a broadened term in an initial search and evaluating the search results relative to the excluded term using categorical or clustered distinctions. Broadening can also include, for example, determining a semantic or conceptual meaning of a query, or one or more search characteristics in a query, and broadening the query based on related semantic or conceptual meanings; such broadening may, but need not, be performed using related search characteristics. Consequently, broadening encompasses analyzing thequery 40 and introducing changes to effect broader search result scope and increased search quality. - By way of example, in a simplest form, one or more words related to at least one of the
original terms 41 could be disjunctively added to theoriginal query 40. Thesearch engine 21 would then execute the expanded query using the broadened list of terms. For instance, theindividual term 41 of “car” could be supplemented with “vehicle” and thesearch engine 21 would execute a search on a query consisting of the terms “car,” “vehicle,” “repair,” and “help.” - By way of further example, the
search engine 21 could initially execute a query, which excludes anoriginal term 41, and map the search results into categories of related terms associated with the excludedoriginal term 41. For instance, theindividual term 41 of “help” could be excluded and thesearch engine 21 would execute a search on a query consisting only of the terms “car” and “repair.” Thesearch engine 21 would then map the resultant search results into specific categories of related terms associated with the term of “help.” Alternatively, thesearch engine 21 could form clusters of search results, rather than mapping the search results into categories. Clusters group search results at a conceptual level, whereas categories group at a literal level of related terms - The foregoing approaches to broadening the
original terms 41 are provided merely as examples of broadening techniques and are not meant to limit or restrict the scope of the invention. Other broadening techniques could be used in addition to or in combinations with the foregoing techniques, which can be selected and combined in various arrangements, as would be recognized by one skilled in the art. - Referring first to
FIG. 3A , theindividual term 41 of “car” has been broadened to include the list of broadenedterms 43 consisting of “cars,” “autos,” and “auto.” Each of the sample broadened terms have been selected from a set of words having a synonymous, alternate spelling, common root, or similar semantic meaning, although other selections of broadened terms could be used in addition to or in combination with the foregoing words set, which can be selected and combined in various styles and arrangements, as would be recognized by one skilled in the art. - Referring next to
FIG. 3B , theindividual term 41 of “help” has been broadened to include the list of broadenedterms 44 consisting of “guide,” “tips,” “tutorial,” and “problem.” Each of the sample broadened terms have been selected from a set of words having a related conceptual meaning, although other selections of broadened terms could be used in addition to or in combination with the foregoing words set, which can be selected and combined in various styles and arrangements, as would be recognized by one skilled in the art. - In a further embodiment, each
individual term 41 being broadened could be provided as a hyperlink, either with or without the broadenedterms list term selected term 41 is broadened upon selection by the user. - In a further embodiment (not shown),
individual terms 41 are logically grouped with one or more otherindividual terms 41 to form a compound term and a set of broadened terms is determined for the compound term. For example, theterm 41 of “hot” followed by the term of “dog” could be logically grouped to form the compound term “hot dog” and a list of broadened terms could include theterm 41 of “hamburger,” “wiener,” and “sausage.” Other forms of identifying and combiningindividual terms 41 to logically form compound and complex terms are possible, as would be recognized by one skilled in the art. - Both broadened terms lists 43, 44 are presented as a static list, although other forms of presentation, including a menu of selectable terms, a list of selectable terms, a set of checkboxes, and a set of hyperlinks corresponding to each broadened term, either with or without a broadened terms list, could be used, as would be recognized by one skilled in the art. Note that such a user interface allowing selection of one or more choices may also be employed to restrict or focus queries, as opposed to broadening queries. For example, from a query containing the term “vegetarian,” the choices “ovo lacto,” “lacto” and “vegan” may be generated, and a user may be allowed to select one or more of the choices to focus an original query.
- Finally, a “select all”
option 42 is provided supplemental to theindividual terms list 41. Selection of the “select all”option 42 triggers the selection of each of the broadened terms lists 43, 44, although other forms of full or partial broadened terms selection are possible, as would be recognized by one skilled in the art. - Sample Screen Shot
-
FIG. 4 is a screen shot showing, by way of example, aWeb page 50 containingsearch results 52 retrieved through broadened search characteristics. The search query constitutes a list ofindividual terms 41 consisting of “car,” “repair,” and “help.” The example search results 52 match the list of terms consisting of “auto,” “guide,” and “problems,” reflecting a broadening of theterms 41 of “car” and “help.” An operator ‘˜’ is prepended to the terms “car” and “help” to expressly request query broadening with respect to the indicated terms. Optionally, a delimiter, such as the ‘/’ character, can be provided with the operator ‘˜’ to signal an ordinary meaning with respect to the operator. For example, the term “/˜car” would signal that the term should be treated by thesearch engine 21 as consisting of “/˜car” without query broadening. - In a further embodiment, every search characteristic, including
term 41, in asearch query 40 could be automatically broadened by including at least one occurrence of the operator within thesearch query 40. - In a further embodiment, the use of an operator is optional and search query broadening is instead requested through the use of hyperlinks associated with one or more individual search characteristics, including
terms 41. Thus, a search characteristic would be broadened upon selection of the associated hyperlink by the user. Other forms of operators and delimiters are possible, as would be recognized by one skilled in the art. - In a further embodiment, the use of the operator ‘˜’ includes an assignable strength, which could be, for example, indicated through repetition of the operator or through the use of alternative operators. For example, the
term 41 of “˜˜help” appearing with two occurrences of the operator ‘˜’ would indicate that theterm 41 should be broadened further than indicated by the occurrence of a single operator ‘˜.’ The types of further broadening include broadening based on categories or clusters of related search characteristics, as well as other forms of broadening, as are known in the art. In addition, other types and forms of operators to indicate an assignable strength are possible, as would be recognized by one skilled in the art. - Method Overview
-
FIG. 5 is a flow diagram showing amethod 40 for providing a user interface with search query broadening, in accordance with the present invention. Themethod 40 is described as a sequence of process operations or steps, which can be executed, for instance, by a search engine 21 (shown inFIG. 1 ). - A
search query 40 is accepted from auser 19 and parsed into individual search characteristics (block 61). Thesearch query 40 is broadened (block 62), as further described below with reference toFIG. 6 . For example, one or more of the search characteristics in thesearch query 40 could be broadened. If specified (block 63), the broadened search query is then presented to the user 19 (block 64), as further described below with reference toFIG. 7 . Following any further query modifications by theuser 19, the query is executed (block 65) to identifyWeb content 22 best matching the search characteristics, such as described in S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Search Engine” (1998) and in U.S. Pat. No. 6,285,999, issued Sep. 6, 2001 to Page, referenced above. Other types and forms of query execution are possible, as is known in the art. Finally, the search results 52 are presented via the browser 18 (block 66). Typically, only a part of the search results 52 need be presented since the full set ofsearch results 52 can exceed available presentation space on thebrowser 18. The method then terminates. - Broadening Query
-
FIG. 6 is a flow diagram showing a routine 70 for broadening a query for use in the method ofFIG. 5 . The purpose of this routine is to identify, broaden and provide a broadenedsearch query 40, such as by broadening individual search characteristics occurring in asearch query 40, including query terms 41. - Each search characteristic in the
search query 40 is iteratively processed (blocks 71-77) as follows, although other forms of non-iterative processing are possible, as would be recognized by one skilled in the art. Each search characteristic is evaluated to determine if an operator expressly requesting query broadening, such as an operator ‘˜,’ is included (block 72). If no operator is included, the search characteristic is not broadened and the next search characteristic is processed (block 77). If an operator is included, the search characteristic is further evaluated to determine if a delimiter signaling ordinary meaning with respect to the operator is included (block 73). If a delimiter is included, the search characteristic is not broadened and the next search characteristic is processed (block 77). If no delimiter is included, the search characteristic is evaluated to determine if the search characteristic should be logically grouped with one or more other search characteristics in thesearch query 40 to form a compound search characteristic (block 74). If the search characteristic should not be logically grouped, broadening search characteristics are provided for just the individual search characteristic (block 75). Otherwise, if the search characteristic should be logically grouped, broadening search characteristics are provided for the compound search characteristic (block 76). - In the described embodiment, the set of broadening search characteristics are generated by receiving one or more example search characteristics corresponding to each search characteristic to be broadened. Weights are assigned to each example search characteristic and a list of broadened search characteristics is formed based on the example search characteristics and the weights assigned to each example search characteristic, such as described in related U.S. patent application Ser. No. 10/425,819, filed Apr. 30, 2003, pending, the disclosure of which is incorporated by reference. Other approaches to generating the broadening search characteristics set are possible, as would be recognized by one skilled in the art.
- Note other approaches to broadening could be used to modify the scope of the
search query 40, such that the search results reflect an increased breadth, rather than a narrowing, limiting, or otherwise restricting of the search scope, as described above with reference toFIGS. 3A-3B . Following search query broadening (blocks 75 and 76), the next search characteristic is processed (block 77), after which the routine returns. - Presenting a Broadened Query
-
FIG. 7 is a flow diagram showing a routine 60 for presenting a broadened query for use in the method ofFIG. 5 . The purpose of this routine is to flexibly present the set of broadened search characteristics to the user relative to the set of search characteristics from theoriginal search query 40. - Each search characteristic in the search query, both original and broadened, is iteratively processed (blocks 81-94) as follows, although other forms of non-iterative processing are possible, as would be recognized by one skilled in the art. If a hyperlink presentation form is desired (block 82), the search characteristic is presented as a hyperlink (block 83), either with or without a broadened search characteristics list. In list form, each broadened search characteristic forms a part of the broadened query upon selection by the user. In non-list form, each selected search characteristic is broadened upon selection by the user. If a static list presentation form is desired (block 84), the search characteristic is presented as part of a static list (block 84). If a menu presentation form is desired (block 86), the search characteristic is presented as part of a menu of selectable search characteristics (block 84) and the search characteristic forms a part of the broadened query upon selection by the user. If a selectable list presentation form is desired (block 88), the search characteristic is presented as part of a list of selectable search characteristics (block 89) and the search characteristic forms a part of the broadened query upon selection by the user. If a checkbox list presentation form is desired (block 90), each broadened search characteristic is presented as part of a list of checkbox selectable search characteristics (block 92) and the search characteristic forms a part of the broadened query upon selection by the user. Finally, if a “select all” option is included (block 92), a “select all” option is presented (block 93). Alternative forms of presentation and selection can be provided in addition to or in combination with the foregoing presentation forms, which can be selected and combined in various arrangements, as would be recognized by one skilled in the art. In addition, the broadened query need not necessarily be presented to the user and the broadened search results could be provided transparently without first presenting the broadened search characteristics. Following presentation form selection (blocks 82, 84, 86, 88, 90), the next search characteristic 41 is processed (block 94), after which the routine returns.
- While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (36)
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Cited By (91)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040186828A1 (en) * | 2002-12-24 | 2004-09-23 | Prem Yadav | Systems and methods for enabling a user to find information of interest to the user |
US20050060310A1 (en) * | 2003-09-12 | 2005-03-17 | Simon Tong | Methods and systems for improving a search ranking using population information |
US20050080776A1 (en) * | 2003-08-21 | 2005-04-14 | Matthew Colledge | Internet searching using semantic disambiguation and expansion |
US20050102278A1 (en) * | 2003-11-12 | 2005-05-12 | Microsoft Corporation | Expanded search keywords |
US20050149499A1 (en) * | 2003-12-30 | 2005-07-07 | Google Inc., A Delaware Corporation | Systems and methods for improving search quality |
US20050278287A1 (en) * | 2004-06-10 | 2005-12-15 | International Business Machines Corporation | Query meaning determination through a grid service |
US20060059492A1 (en) * | 2004-09-14 | 2006-03-16 | International Business Machines Corporation | Determining a capacity of a grid environment to handle a required workload for a virtual grid job request |
US20060080292A1 (en) * | 2004-10-08 | 2006-04-13 | Alanzi Faisal Saud M | Enhanced interface utility for web-based searching |
US20060149576A1 (en) * | 2005-01-06 | 2006-07-06 | Ernest Leslie M | Managing compliance with service level agreements in a grid environment |
US20060150190A1 (en) * | 2005-01-06 | 2006-07-06 | Gusler Carl P | Setting operation based resource utilization thresholds for resource use by a process |
US20060150158A1 (en) * | 2005-01-06 | 2006-07-06 | Fellenstein Craig W | Facilitating overall grid environment management by monitoring and distributing grid activity |
US20060195435A1 (en) * | 2005-02-28 | 2006-08-31 | Microsoft Corporation | System and method for providing query assistance |
US20060224554A1 (en) * | 2005-03-29 | 2006-10-05 | Bailey David R | Query revision using known highly-ranked queries |
US20060230005A1 (en) * | 2005-03-30 | 2006-10-12 | Bailey David R | Empirical validation of suggested alternative queries |
US20060230022A1 (en) * | 2005-03-29 | 2006-10-12 | Bailey David R | Integration of multiple query revision models |
US20060230035A1 (en) * | 2005-03-30 | 2006-10-12 | Bailey David R | Estimating confidence for query revision models |
US20070100799A1 (en) * | 2005-10-31 | 2007-05-03 | Rose Daniel E | Methods for navigating collections of information in varying levels of detail |
US20080215416A1 (en) * | 2007-01-31 | 2008-09-04 | Collarity, Inc. | Searchable interactive internet advertisements |
US20080222024A1 (en) * | 2005-01-12 | 2008-09-11 | International Business Machines Corporation | Automatically distributing a bid request for a grid job to multiple grid providers and analyzing responses to select a winning grid provider |
US20080307250A1 (en) * | 2005-01-12 | 2008-12-11 | International Business Machines Corporation | Managing network errors communicated in a message transaction with error information using a troubleshooting agent |
US20090013222A1 (en) * | 2004-01-14 | 2009-01-08 | International Business Machines Corporation | Managing analysis of a degraded service in a grid environment |
US7505964B2 (en) | 2003-09-12 | 2009-03-17 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US20090132703A1 (en) * | 2005-01-06 | 2009-05-21 | International Business Machines Corporation | Verifying resource functionality before use by a grid job submitted to a grid environment |
US20090138594A1 (en) * | 2005-01-06 | 2009-05-28 | International Business Machines Corporation | Coordinating the monitoring, management, and prediction of unintended changes within a grid environment |
US20090157342A1 (en) * | 2007-10-29 | 2009-06-18 | China Mobile Communication Corp. Design Institute | Method and apparatus of using drive test data for propagation model calibration |
US20090276419A1 (en) * | 2008-05-01 | 2009-11-05 | Chacha Search Inc. | Method and system for improvement of request processing |
US7636714B1 (en) | 2005-03-31 | 2009-12-22 | Google Inc. | Determining query term synonyms within query context |
US20100017392A1 (en) * | 2008-07-18 | 2010-01-21 | Jianwei Dian | Intent match search engine |
US20100049770A1 (en) * | 2008-06-26 | 2010-02-25 | Collarity, Inc. | Interactions among online digital identities |
US7707288B2 (en) | 2005-01-06 | 2010-04-27 | International Business Machines Corporation | Automatically building a locally managed virtual node grouping to handle a grid job requiring a degree of resource parallelism within a grid environment |
US20100293180A1 (en) * | 2009-05-18 | 2010-11-18 | Microsoft Corporation | Identifying conceptually related terms in search query results |
US20110035403A1 (en) * | 2005-12-05 | 2011-02-10 | Emil Ismalon | Generation of refinement terms for search queries |
US7925657B1 (en) | 2004-03-17 | 2011-04-12 | Google Inc. | Methods and systems for adjusting a scoring measure based on query breadth |
US7937396B1 (en) | 2005-03-23 | 2011-05-03 | Google Inc. | Methods and systems for identifying paraphrases from an index of information items and associated sentence fragments |
US7937265B1 (en) | 2005-09-27 | 2011-05-03 | Google Inc. | Paraphrase acquisition |
US20110161242A1 (en) * | 2009-12-28 | 2011-06-30 | Rovi Technologies Corporation | Systems and methods for searching and browsing media in an interactive media guidance application |
US20110184726A1 (en) * | 2010-01-25 | 2011-07-28 | Connor Robert A | Morphing text by splicing end-compatible segments |
US20110295897A1 (en) * | 2010-06-01 | 2011-12-01 | Microsoft Corporation | Query correction probability based on query-correction pairs |
US20110313756A1 (en) * | 2010-06-21 | 2011-12-22 | Connor Robert A | Text sizer (TM) |
US8136118B2 (en) | 2004-01-14 | 2012-03-13 | International Business Machines Corporation | Maintaining application operations within a suboptimal grid environment |
US20120158705A1 (en) * | 2010-12-16 | 2012-06-21 | Microsoft Corporation | Local search using feature backoff |
US8275881B2 (en) | 2004-01-13 | 2012-09-25 | International Business Machines Corporation | Managing escalating resource needs within a grid environment |
US8326866B1 (en) | 2006-10-24 | 2012-12-04 | Google Inc. | Using geographic data to identify correlated geographic synonyms |
US8346792B1 (en) | 2010-11-09 | 2013-01-01 | Google Inc. | Query generation using structural similarity between documents |
US8346791B1 (en) | 2008-05-16 | 2013-01-01 | Google Inc. | Search augmentation |
US8346591B2 (en) | 2005-01-12 | 2013-01-01 | International Business Machines Corporation | Automating responses by grid providers to bid requests indicating criteria for a grid job |
US8387058B2 (en) | 2004-01-13 | 2013-02-26 | International Business Machines Corporation | Minimizing complex decisions to allocate additional resources to a job submitted to a grid environment |
US8392441B1 (en) | 2009-08-15 | 2013-03-05 | Google Inc. | Synonym generation using online decompounding and transitivity |
US8396865B1 (en) | 2008-12-10 | 2013-03-12 | Google Inc. | Sharing search engine relevance data between corpora |
US8396757B2 (en) | 2005-01-12 | 2013-03-12 | International Business Machines Corporation | Estimating future grid job costs by classifying grid jobs and storing results of processing grid job microcosms |
US8442972B2 (en) | 2006-10-11 | 2013-05-14 | Collarity, Inc. | Negative associations for search results ranking and refinement |
US8498974B1 (en) | 2009-08-31 | 2013-07-30 | Google Inc. | Refining search results |
US8521725B1 (en) * | 2003-12-03 | 2013-08-27 | Google Inc. | Systems and methods for improved searching |
US8583650B2 (en) | 2005-01-06 | 2013-11-12 | International Business Machines Corporation | Automated management of software images for efficient resource node building within a grid environment |
US8615514B1 (en) | 2010-02-03 | 2013-12-24 | Google Inc. | Evaluating website properties by partitioning user feedback |
US8626785B2 (en) | 2007-12-07 | 2014-01-07 | Google Inc. | Contextual query revision |
US8661029B1 (en) | 2006-11-02 | 2014-02-25 | Google Inc. | Modifying search result ranking based on implicit user feedback |
WO2014035734A1 (en) * | 2012-08-27 | 2014-03-06 | Microsoft Corporation | Semantic query language |
WO2014042556A1 (en) * | 2012-09-12 | 2014-03-20 | Ikonomov Artashes Valeryevich | System for performing a personalized information search |
US8694374B1 (en) | 2007-03-14 | 2014-04-08 | Google Inc. | Detecting click spam |
US8694511B1 (en) | 2007-08-20 | 2014-04-08 | Google Inc. | Modifying search result ranking based on populations |
US8832083B1 (en) | 2010-07-23 | 2014-09-09 | Google Inc. | Combining user feedback |
US8875038B2 (en) | 2010-01-19 | 2014-10-28 | Collarity, Inc. | Anchoring for content synchronization |
US8874555B1 (en) | 2009-11-20 | 2014-10-28 | Google Inc. | Modifying scoring data based on historical changes |
US8903810B2 (en) | 2005-12-05 | 2014-12-02 | Collarity, Inc. | Techniques for ranking search results |
US8909655B1 (en) | 2007-10-11 | 2014-12-09 | Google Inc. | Time based ranking |
US8924379B1 (en) | 2010-03-05 | 2014-12-30 | Google Inc. | Temporal-based score adjustments |
US8938463B1 (en) | 2007-03-12 | 2015-01-20 | Google Inc. | Modifying search result ranking based on implicit user feedback and a model of presentation bias |
US20150039579A1 (en) * | 2013-07-31 | 2015-02-05 | International Business Machines Corporation | Search query obfuscation via broadened subqueries and recombining |
US8959093B1 (en) | 2010-03-15 | 2015-02-17 | Google Inc. | Ranking search results based on anchors |
US8972394B1 (en) | 2009-07-20 | 2015-03-03 | Google Inc. | Generating a related set of documents for an initial set of documents |
US8972391B1 (en) | 2009-10-02 | 2015-03-03 | Google Inc. | Recent interest based relevance scoring |
US9002867B1 (en) | 2010-12-30 | 2015-04-07 | Google Inc. | Modifying ranking data based on document changes |
US9009146B1 (en) | 2009-04-08 | 2015-04-14 | Google Inc. | Ranking search results based on similar queries |
US9092510B1 (en) | 2007-04-30 | 2015-07-28 | Google Inc. | Modifying search result ranking based on a temporal element of user feedback |
US9183499B1 (en) | 2013-04-19 | 2015-11-10 | Google Inc. | Evaluating quality based on neighbor features |
US9223868B2 (en) | 2004-06-28 | 2015-12-29 | Google Inc. | Deriving and using interaction profiles |
US9298700B1 (en) * | 2009-07-28 | 2016-03-29 | Amazon Technologies, Inc. | Determining similar phrases |
US9485286B1 (en) | 2010-03-02 | 2016-11-01 | Amazon Technologies, Inc. | Sharing media items with pass phrases |
CN106095912A (en) * | 2016-06-08 | 2016-11-09 | 北京百度网讯科技有限公司 | For the method and apparatus generating expanding query word |
US9501469B2 (en) | 2012-11-21 | 2016-11-22 | University Of Massachusetts | Analogy finder |
US9569770B1 (en) | 2009-01-13 | 2017-02-14 | Amazon Technologies, Inc. | Generating constructed phrases |
US9623119B1 (en) | 2010-06-29 | 2017-04-18 | Google Inc. | Accentuating search results |
US9734244B2 (en) | 2014-12-08 | 2017-08-15 | Rovi Guides, Inc. | Methods and systems for providing serendipitous recommendations |
US20180018389A1 (en) * | 2015-02-02 | 2018-01-18 | Alibaba Group Holding Limited | Method and apparatus for keyword-based text retrieval |
US10007712B1 (en) | 2009-08-20 | 2018-06-26 | Amazon Technologies, Inc. | Enforcing user-specified rules |
CN108334487A (en) * | 2017-07-14 | 2018-07-27 | 腾讯科技(深圳)有限公司 | Lack semantics information complementing method, device, computer equipment and storage medium |
US10115084B2 (en) | 2012-10-10 | 2018-10-30 | Artashes Valeryevich Ikonomov | Electronic payment system |
US20180365216A1 (en) * | 2017-06-20 | 2018-12-20 | The Boeing Company | Text mining a dataset of electronic documents to discover terms of interest |
US10162869B2 (en) | 2009-09-04 | 2018-12-25 | Microsoft Technology Licensing, Llc | Table of contents for search query refinement |
US11868883B1 (en) * | 2010-10-26 | 2024-01-09 | Michael Lamport Commons | Intelligent control with hierarchical stacked neural networks |
Families Citing this family (81)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8645137B2 (en) | 2000-03-16 | 2014-02-04 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US8019749B2 (en) * | 2005-03-17 | 2011-09-13 | Roy Leban | System, method, and user interface for organizing and searching information |
US8438142B2 (en) | 2005-05-04 | 2013-05-07 | Google Inc. | Suggesting and refining user input based on original user input |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
US9495358B2 (en) | 2006-10-10 | 2016-11-15 | Abbyy Infopoisk Llc | Cross-language text clustering |
US9235573B2 (en) | 2006-10-10 | 2016-01-12 | Abbyy Infopoisk Llc | Universal difference measure |
KR20080096005A (en) * | 2007-04-26 | 2008-10-30 | 엔에이치엔(주) | Keyword providing method and system according to keyword providing scope |
US20090063632A1 (en) * | 2007-08-31 | 2009-03-05 | Searete Llc, A Limited Liability Corporation Of The State Of Delaware | Layering prospective activity information |
CN100514337C (en) * | 2007-09-10 | 2009-07-15 | 腾讯科技(深圳)有限公司 | Association information generating system of key words and generation method thereof |
US8583633B2 (en) * | 2007-11-30 | 2013-11-12 | Ebay Inc. | Using reputation measures to improve search relevance |
JP4981705B2 (en) * | 2008-02-13 | 2012-07-25 | ヤフー株式会社 | Information search method, search device, and search program |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
UA90764C2 (en) * | 2008-05-13 | 2010-05-25 | Сергей игоревич Вакарин | Information object search method and system to realize it |
RU2414745C2 (en) * | 2008-10-06 | 2011-03-20 | Петр Михайлович Мурашев | Search method |
IT1391936B1 (en) * | 2008-10-20 | 2012-02-02 | Facility Italia S R L | METHOD OF SEARCHING FOR MULTIMEDIA CONTENT IN THE INTERNET. |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US8994660B2 (en) | 2011-08-29 | 2015-03-31 | Apple Inc. | Text correction processing |
RU2538278C2 (en) * | 2012-01-26 | 2015-01-10 | Ольга Алексеевна Алексютина | Method of creating database |
US9280610B2 (en) | 2012-05-14 | 2016-03-08 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
WO2014014374A1 (en) | 2012-07-19 | 2014-01-23 | Yandex Europe Ag | Search query suggestions based in part on a prior search |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197336A1 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
KR101922663B1 (en) | 2013-06-09 | 2018-11-28 | 애플 인크. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
RU2605001C2 (en) * | 2014-06-24 | 2016-12-20 | Общество С Ограниченной Ответственностью "Яндекс" | Method for processing user's search request and server used therein |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
KR102254329B1 (en) * | 2014-10-27 | 2021-05-21 | 삼성에스디에스 주식회사 | Method and Apparatus for Providing User Customized Search Result |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9578173B2 (en) | 2015-06-05 | 2017-02-21 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
CN106547794B (en) * | 2015-09-22 | 2020-04-14 | 阿里巴巴集团控股有限公司 | Information searching method and device |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179588B1 (en) | 2016-06-09 | 2019-02-22 | Apple Inc. | Intelligent automated assistant in a home environment |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
DK201770383A1 (en) | 2017-05-09 | 2018-12-14 | Apple Inc. | User interface for correcting recognition errors |
DK201770439A1 (en) | 2017-05-11 | 2018-12-13 | Apple Inc. | Offline personal assistant |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770429A1 (en) | 2017-05-12 | 2018-12-14 | Apple Inc. | Low-latency intelligent automated assistant |
DK201770432A1 (en) | 2017-05-15 | 2018-12-21 | Apple Inc. | Hierarchical belief states for digital assistants |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
DK179560B1 (en) | 2017-05-16 | 2019-02-18 | Apple Inc. | Far-field extension for digital assistant services |
US10769164B2 (en) * | 2017-12-06 | 2020-09-08 | Sap Se | Simplified access for core business with enterprise search |
US11567980B2 (en) | 2018-05-07 | 2023-01-31 | Google Llc | Determining responsive content for a compound query based on a set of generated sub-queries |
Citations (49)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5056021A (en) * | 1989-06-08 | 1991-10-08 | Carolyn Ausborn | Method and apparatus for abstracting concepts from natural language |
US5488725A (en) * | 1991-10-08 | 1996-01-30 | West Publishing Company | System of document representation retrieval by successive iterated probability sampling |
US5696962A (en) * | 1993-06-24 | 1997-12-09 | Xerox Corporation | Method for computerized information retrieval using shallow linguistic analysis |
US5721902A (en) * | 1995-09-15 | 1998-02-24 | Infonautics Corporation | Restricted expansion of query terms using part of speech tagging |
US5778355A (en) * | 1996-06-11 | 1998-07-07 | International Business Machines Corp. | Database method and apparatus for interactively retrieving data members and related members from a collection of data |
US5842203A (en) * | 1995-12-01 | 1998-11-24 | International Business Machines Corporation | Method and system for performing non-boolean search queries in a graphical user interface |
US5953718A (en) * | 1997-11-12 | 1999-09-14 | Oracle Corporation | Research mode for a knowledge base search and retrieval system |
US5983237A (en) * | 1996-03-29 | 1999-11-09 | Virage, Inc. | Visual dictionary |
US5982370A (en) * | 1997-07-18 | 1999-11-09 | International Business Machines Corporation | Highlighting tool for search specification in a user interface of a computer system |
US5995959A (en) * | 1997-01-24 | 1999-11-30 | The Board Of Regents Of The University Of Washington | Method and system for network information access |
US6012053A (en) * | 1997-06-23 | 2000-01-04 | Lycos, Inc. | Computer system with user-controlled relevance ranking of search results |
US6014662A (en) * | 1997-11-26 | 2000-01-11 | International Business Machines Corporation | Configurable briefing presentations of search results on a graphical interface |
US6026388A (en) * | 1995-08-16 | 2000-02-15 | Textwise, Llc | User interface and other enhancements for natural language information retrieval system and method |
US6055528A (en) * | 1997-07-25 | 2000-04-25 | Claritech Corporation | Method for cross-linguistic document retrieval |
US6098081A (en) * | 1996-05-06 | 2000-08-01 | Microsoft Corporation | Hypermedia navigation using soft hyperlinks |
US6154747A (en) * | 1998-08-26 | 2000-11-28 | Hunt; Rolf G. | Hash table implementation of an object repository |
US6173275B1 (en) * | 1993-09-20 | 2001-01-09 | Hnc Software, Inc. | Representation and retrieval of images using context vectors derived from image information elements |
US6175829B1 (en) * | 1998-04-22 | 2001-01-16 | Nec Usa, Inc. | Method and apparatus for facilitating query reformulation |
US6243713B1 (en) * | 1998-08-24 | 2001-06-05 | Excalibur Technologies Corp. | Multimedia document retrieval by application of multimedia queries to a unified index of multimedia data for a plurality of multimedia data types |
US6285999B1 (en) * | 1997-01-10 | 2001-09-04 | The Board Of Trustees Of The Leland Stanford Junior University | Method for node ranking in a linked database |
US20010047355A1 (en) * | 2000-03-16 | 2001-11-29 | Anwar Mohammed S. | System and method for analyzing a query and generating results and related questions |
US6341277B1 (en) * | 1998-11-17 | 2002-01-22 | International Business Machines Corporation | System and method for performance complex heterogeneous database queries using a single SQL expression |
US20020022955A1 (en) * | 2000-04-03 | 2002-02-21 | Galina Troyanova | Synonym extension of search queries with validation |
US6363377B1 (en) * | 1998-07-30 | 2002-03-26 | Sarnoff Corporation | Search data processor |
US6385600B1 (en) * | 1997-04-03 | 2002-05-07 | At&T Corp. | System and method for searching on a computer using an evidence set |
US20020059161A1 (en) * | 1998-11-03 | 2002-05-16 | Wen-Syan Li | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US6418445B1 (en) * | 1998-03-06 | 2002-07-09 | Perot Systems Corporation | System and method for distributed data collection and storage |
US6446061B1 (en) * | 1998-07-31 | 2002-09-03 | International Business Machines Corporation | Taxonomy generation for document collections |
US20020169771A1 (en) * | 2001-05-09 | 2002-11-14 | Melmon Kenneth L. | System & method for facilitating knowledge management |
US6510406B1 (en) * | 1999-03-23 | 2003-01-21 | Mathsoft, Inc. | Inverse inference engine for high performance web search |
US20030069880A1 (en) * | 2001-09-24 | 2003-04-10 | Ask Jeeves, Inc. | Natural language query processing |
US6560597B1 (en) * | 2000-03-21 | 2003-05-06 | International Business Machines Corporation | Concept decomposition using clustering |
US6581052B1 (en) * | 1998-05-14 | 2003-06-17 | Microsoft Corporation | Test generator for database management systems |
US6618733B1 (en) * | 2000-04-11 | 2003-09-09 | Revelink Inc. | View navigation for creation, update and querying of data objects and textual annotations of relations between data objects |
US6636377B1 (en) * | 2000-06-30 | 2003-10-21 | Western Digital Technologies, Inc. | Method of tuning feed-forward control in a disk drive |
US20030212666A1 (en) * | 2002-05-10 | 2003-11-13 | Sankar Basu | Adaptive probabilistic query expansion |
US20040002963A1 (en) * | 2002-06-28 | 2004-01-01 | Cynkin Laurence H. | Resolving query terms based on time of submission |
US6675159B1 (en) * | 2000-07-27 | 2004-01-06 | Science Applic Int Corp | Concept-based search and retrieval system |
US6701305B1 (en) * | 1999-06-09 | 2004-03-02 | The Boeing Company | Methods, apparatus and computer program products for information retrieval and document classification utilizing a multidimensional subspace |
US6701310B1 (en) * | 1999-11-22 | 2004-03-02 | Nec Corporation | Information search device and information search method using topic-centric query routing |
US6711585B1 (en) * | 1999-06-15 | 2004-03-23 | Kanisa Inc. | System and method for implementing a knowledge management system |
US6728700B2 (en) * | 1996-04-23 | 2004-04-27 | International Business Machines Corporation | Natural language help interface |
US20040098377A1 (en) * | 2002-11-16 | 2004-05-20 | International Business Machines Corporation | System and method for conducting adaptive search using a peer-to-peer network |
US20040158560A1 (en) * | 2003-02-12 | 2004-08-12 | Ji-Rong Wen | Systems and methods for query expansion |
US6847966B1 (en) * | 2002-04-24 | 2005-01-25 | Engenium Corporation | Method and system for optimally searching a document database using a representative semantic space |
US6947930B2 (en) * | 2003-03-21 | 2005-09-20 | Overture Services, Inc. | Systems and methods for interactive search query refinement |
US7213011B1 (en) * | 2002-04-08 | 2007-05-01 | Oracle International Corporation | Efficient processing of multi-column and function-based in-list predicates |
US7370040B1 (en) * | 2000-11-21 | 2008-05-06 | Microsoft Corporation | Searching with adaptively configurable user interface and extensible query language |
US7403939B1 (en) * | 2003-05-30 | 2008-07-22 | Aol Llc | Resolving queries based on automatic determination of requestor geographic location |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5617566A (en) | 1993-12-10 | 1997-04-01 | Cheyenne Advanced Technology Ltd. | File portion logging and arching by means of an auxilary database |
RU2102789C1 (en) | 1994-11-24 | 1998-01-20 | Московское конструкторское бюро "Параллель" | Device for information retrieval |
JP2000331012A (en) | 1999-05-19 | 2000-11-30 | Oki Electric Ind Co Ltd | Electronic document retrieval method |
RU2150147C1 (en) | 1999-06-15 | 2000-05-27 | Воробьев Вадим Федорович | Method for learning foreign languages |
WO2000079436A2 (en) | 1999-06-24 | 2000-12-28 | Simpli.Com | Search engine interface |
RU12738U1 (en) | 1999-10-07 | 2000-01-27 | Валдин Владимир Владимирович | INFORMATION AND SEARCH SYSTEM |
US20020165856A1 (en) | 2001-05-04 | 2002-11-07 | Gilfillan Lynne E. | Collaborative research systems |
JP2003030228A (en) | 2001-07-12 | 2003-01-31 | Casio Comput Co Ltd | Information retrieval system, information retrieval method, and program |
JP2003030235A (en) | 2001-07-12 | 2003-01-31 | Casio Comput Co Ltd | Information retrieval system, information retrieval method, and program |
US20030018468A1 (en) * | 2001-07-20 | 2003-01-23 | Johnson Deanna G. | Universal search engine |
RU2199148C1 (en) | 2001-07-20 | 2003-02-20 | Военный университет связи | Data retrieval device |
CN1335574A (en) * | 2001-09-05 | 2002-02-13 | 罗笑南 | Intelligent semantic searching method |
CN1193309C (en) * | 2001-12-29 | 2005-03-16 | 财团法人资讯工业策进会 | System and method for establishing association of search engine keywords |
US20040064447A1 (en) * | 2002-09-27 | 2004-04-01 | Simske Steven J. | System and method for management of synonymic searching |
-
2003
- 2003-07-28 US US10/629,479 patent/US8856163B2/en active Active
-
2004
- 2004-07-27 JP JP2006522020A patent/JP4731479B2/en not_active Expired - Fee Related
- 2004-07-27 AU AU2004262352A patent/AU2004262352C1/en not_active Ceased
- 2004-07-27 CN CNB200480025815XA patent/CN100501730C/en not_active Expired - Fee Related
- 2004-07-27 CA CA2533605A patent/CA2533605C/en not_active Expired - Fee Related
- 2004-07-27 WO PCT/US2004/024306 patent/WO2005013153A1/en active Application Filing
- 2004-07-27 RU RU2006106176/09A patent/RU2324220C2/en not_active IP Right Cessation
- 2004-07-27 EP EP04779373A patent/EP1654681A1/en not_active Ceased
-
2006
- 2006-11-17 HK HK06112658.2A patent/HK1092238A1/en not_active IP Right Cessation
-
2008
- 2008-01-10 RU RU2008101375/08A patent/RU2460131C2/en not_active IP Right Cessation
-
2012
- 2012-09-14 US US13/616,889 patent/US20130159338A1/en not_active Abandoned
Patent Citations (51)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5056021A (en) * | 1989-06-08 | 1991-10-08 | Carolyn Ausborn | Method and apparatus for abstracting concepts from natural language |
US5488725A (en) * | 1991-10-08 | 1996-01-30 | West Publishing Company | System of document representation retrieval by successive iterated probability sampling |
US5696962A (en) * | 1993-06-24 | 1997-12-09 | Xerox Corporation | Method for computerized information retrieval using shallow linguistic analysis |
US6173275B1 (en) * | 1993-09-20 | 2001-01-09 | Hnc Software, Inc. | Representation and retrieval of images using context vectors derived from image information elements |
US6026388A (en) * | 1995-08-16 | 2000-02-15 | Textwise, Llc | User interface and other enhancements for natural language information retrieval system and method |
US5721902A (en) * | 1995-09-15 | 1998-02-24 | Infonautics Corporation | Restricted expansion of query terms using part of speech tagging |
US5842203A (en) * | 1995-12-01 | 1998-11-24 | International Business Machines Corporation | Method and system for performing non-boolean search queries in a graphical user interface |
US5983237A (en) * | 1996-03-29 | 1999-11-09 | Virage, Inc. | Visual dictionary |
US6728700B2 (en) * | 1996-04-23 | 2004-04-27 | International Business Machines Corporation | Natural language help interface |
US6098081A (en) * | 1996-05-06 | 2000-08-01 | Microsoft Corporation | Hypermedia navigation using soft hyperlinks |
US5778355A (en) * | 1996-06-11 | 1998-07-07 | International Business Machines Corp. | Database method and apparatus for interactively retrieving data members and related members from a collection of data |
US6285999B1 (en) * | 1997-01-10 | 2001-09-04 | The Board Of Trustees Of The Leland Stanford Junior University | Method for node ranking in a linked database |
US5995959A (en) * | 1997-01-24 | 1999-11-30 | The Board Of Regents Of The University Of Washington | Method and system for network information access |
US6385600B1 (en) * | 1997-04-03 | 2002-05-07 | At&T Corp. | System and method for searching on a computer using an evidence set |
US6012053A (en) * | 1997-06-23 | 2000-01-04 | Lycos, Inc. | Computer system with user-controlled relevance ranking of search results |
US5982370A (en) * | 1997-07-18 | 1999-11-09 | International Business Machines Corporation | Highlighting tool for search specification in a user interface of a computer system |
US6263329B1 (en) * | 1997-07-25 | 2001-07-17 | Claritech | Method and apparatus for cross-linguistic database retrieval |
US6055528A (en) * | 1997-07-25 | 2000-04-25 | Claritech Corporation | Method for cross-linguistic document retrieval |
US5953718A (en) * | 1997-11-12 | 1999-09-14 | Oracle Corporation | Research mode for a knowledge base search and retrieval system |
US6014662A (en) * | 1997-11-26 | 2000-01-11 | International Business Machines Corporation | Configurable briefing presentations of search results on a graphical interface |
US6418445B1 (en) * | 1998-03-06 | 2002-07-09 | Perot Systems Corporation | System and method for distributed data collection and storage |
US6175829B1 (en) * | 1998-04-22 | 2001-01-16 | Nec Usa, Inc. | Method and apparatus for facilitating query reformulation |
US6581052B1 (en) * | 1998-05-14 | 2003-06-17 | Microsoft Corporation | Test generator for database management systems |
US6363377B1 (en) * | 1998-07-30 | 2002-03-26 | Sarnoff Corporation | Search data processor |
US6446061B1 (en) * | 1998-07-31 | 2002-09-03 | International Business Machines Corporation | Taxonomy generation for document collections |
US6243713B1 (en) * | 1998-08-24 | 2001-06-05 | Excalibur Technologies Corp. | Multimedia document retrieval by application of multimedia queries to a unified index of multimedia data for a plurality of multimedia data types |
US6154747A (en) * | 1998-08-26 | 2000-11-28 | Hunt; Rolf G. | Hash table implementation of an object repository |
US20020059161A1 (en) * | 1998-11-03 | 2002-05-16 | Wen-Syan Li | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US6480843B2 (en) * | 1998-11-03 | 2002-11-12 | Nec Usa, Inc. | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US6341277B1 (en) * | 1998-11-17 | 2002-01-22 | International Business Machines Corporation | System and method for performance complex heterogeneous database queries using a single SQL expression |
US6510406B1 (en) * | 1999-03-23 | 2003-01-21 | Mathsoft, Inc. | Inverse inference engine for high performance web search |
US6701305B1 (en) * | 1999-06-09 | 2004-03-02 | The Boeing Company | Methods, apparatus and computer program products for information retrieval and document classification utilizing a multidimensional subspace |
US6711585B1 (en) * | 1999-06-15 | 2004-03-23 | Kanisa Inc. | System and method for implementing a knowledge management system |
US6701310B1 (en) * | 1999-11-22 | 2004-03-02 | Nec Corporation | Information search device and information search method using topic-centric query routing |
US20010047355A1 (en) * | 2000-03-16 | 2001-11-29 | Anwar Mohammed S. | System and method for analyzing a query and generating results and related questions |
US6560597B1 (en) * | 2000-03-21 | 2003-05-06 | International Business Machines Corporation | Concept decomposition using clustering |
US20020022955A1 (en) * | 2000-04-03 | 2002-02-21 | Galina Troyanova | Synonym extension of search queries with validation |
US6618733B1 (en) * | 2000-04-11 | 2003-09-09 | Revelink Inc. | View navigation for creation, update and querying of data objects and textual annotations of relations between data objects |
US6636377B1 (en) * | 2000-06-30 | 2003-10-21 | Western Digital Technologies, Inc. | Method of tuning feed-forward control in a disk drive |
US6675159B1 (en) * | 2000-07-27 | 2004-01-06 | Science Applic Int Corp | Concept-based search and retrieval system |
US7370040B1 (en) * | 2000-11-21 | 2008-05-06 | Microsoft Corporation | Searching with adaptively configurable user interface and extensible query language |
US20020169771A1 (en) * | 2001-05-09 | 2002-11-14 | Melmon Kenneth L. | System & method for facilitating knowledge management |
US20030069880A1 (en) * | 2001-09-24 | 2003-04-10 | Ask Jeeves, Inc. | Natural language query processing |
US7213011B1 (en) * | 2002-04-08 | 2007-05-01 | Oracle International Corporation | Efficient processing of multi-column and function-based in-list predicates |
US6847966B1 (en) * | 2002-04-24 | 2005-01-25 | Engenium Corporation | Method and system for optimally searching a document database using a representative semantic space |
US20030212666A1 (en) * | 2002-05-10 | 2003-11-13 | Sankar Basu | Adaptive probabilistic query expansion |
US20040002963A1 (en) * | 2002-06-28 | 2004-01-01 | Cynkin Laurence H. | Resolving query terms based on time of submission |
US20040098377A1 (en) * | 2002-11-16 | 2004-05-20 | International Business Machines Corporation | System and method for conducting adaptive search using a peer-to-peer network |
US20040158560A1 (en) * | 2003-02-12 | 2004-08-12 | Ji-Rong Wen | Systems and methods for query expansion |
US6947930B2 (en) * | 2003-03-21 | 2005-09-20 | Overture Services, Inc. | Systems and methods for interactive search query refinement |
US7403939B1 (en) * | 2003-05-30 | 2008-07-22 | Aol Llc | Resolving queries based on automatic determination of requestor geographic location |
Cited By (169)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040186828A1 (en) * | 2002-12-24 | 2004-09-23 | Prem Yadav | Systems and methods for enabling a user to find information of interest to the user |
US7895221B2 (en) * | 2003-08-21 | 2011-02-22 | Idilia Inc. | Internet searching using semantic disambiguation and expansion |
US20050080776A1 (en) * | 2003-08-21 | 2005-04-14 | Matthew Colledge | Internet searching using semantic disambiguation and expansion |
US20110202563A1 (en) * | 2003-08-21 | 2011-08-18 | Idilia Inc. | Internet searching using semantic disambiguation and expansion |
US20090112857A1 (en) * | 2003-09-12 | 2009-04-30 | Google Inc. | Methods and Systems for Improving a Search Ranking Using Related Queries |
US7505964B2 (en) | 2003-09-12 | 2009-03-17 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US20050060310A1 (en) * | 2003-09-12 | 2005-03-17 | Simon Tong | Methods and systems for improving a search ranking using population information |
US7454417B2 (en) | 2003-09-12 | 2008-11-18 | Google Inc. | Methods and systems for improving a search ranking using population information |
US20110238643A1 (en) * | 2003-09-12 | 2011-09-29 | Google Inc. | Methods and systems for improving a search ranking using population information |
US8380705B2 (en) | 2003-09-12 | 2013-02-19 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US8024326B2 (en) | 2003-09-12 | 2011-09-20 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US8452758B2 (en) | 2003-09-12 | 2013-05-28 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US8515951B2 (en) | 2003-09-12 | 2013-08-20 | Google Inc. | Methods and systems for improving a search ranking using population information |
US8510294B2 (en) | 2003-09-12 | 2013-08-13 | Google Inc. | Methods and systems for improving a search ranking using population information |
US8090713B2 (en) | 2003-09-12 | 2012-01-03 | Google Inc. | Methods and systems for improving a search ranking using population information |
US9697249B1 (en) | 2003-09-30 | 2017-07-04 | Google Inc. | Estimating confidence for query revision models |
US20050102278A1 (en) * | 2003-11-12 | 2005-05-12 | Microsoft Corporation | Expanded search keywords |
US8521725B1 (en) * | 2003-12-03 | 2013-08-27 | Google Inc. | Systems and methods for improved searching |
US8914358B1 (en) | 2003-12-03 | 2014-12-16 | Google Inc. | Systems and methods for improved searching |
US20050149499A1 (en) * | 2003-12-30 | 2005-07-07 | Google Inc., A Delaware Corporation | Systems and methods for improving search quality |
US8275881B2 (en) | 2004-01-13 | 2012-09-25 | International Business Machines Corporation | Managing escalating resource needs within a grid environment |
US8387058B2 (en) | 2004-01-13 | 2013-02-26 | International Business Machines Corporation | Minimizing complex decisions to allocate additional resources to a job submitted to a grid environment |
US7734679B2 (en) | 2004-01-14 | 2010-06-08 | International Business Machines Corporation | Managing analysis of a degraded service in a grid environment |
US20090013222A1 (en) * | 2004-01-14 | 2009-01-08 | International Business Machines Corporation | Managing analysis of a degraded service in a grid environment |
US8136118B2 (en) | 2004-01-14 | 2012-03-13 | International Business Machines Corporation | Maintaining application operations within a suboptimal grid environment |
US8060517B2 (en) | 2004-03-17 | 2011-11-15 | Google Inc. | Methods and systems for adjusting a scoring measure based on query breadth |
US7925657B1 (en) | 2004-03-17 | 2011-04-12 | Google Inc. | Methods and systems for adjusting a scoring measure based on query breadth |
US20050278287A1 (en) * | 2004-06-10 | 2005-12-15 | International Business Machines Corporation | Query meaning determination through a grid service |
US7266547B2 (en) * | 2004-06-10 | 2007-09-04 | International Business Machines Corporation | Query meaning determination through a grid service |
US20070250489A1 (en) * | 2004-06-10 | 2007-10-25 | International Business Machines Corporation | Query meaning determination through a grid service |
US7921133B2 (en) * | 2004-06-10 | 2011-04-05 | International Business Machines Corporation | Query meaning determination through a grid service |
US9223868B2 (en) | 2004-06-28 | 2015-12-29 | Google Inc. | Deriving and using interaction profiles |
US10387512B2 (en) | 2004-06-28 | 2019-08-20 | Google Llc | Deriving and using interaction profiles |
US20060059492A1 (en) * | 2004-09-14 | 2006-03-16 | International Business Machines Corporation | Determining a capacity of a grid environment to handle a required workload for a virtual grid job request |
US7712100B2 (en) | 2004-09-14 | 2010-05-04 | International Business Machines Corporation | Determining a capacity of a grid environment to handle a required workload for a virtual grid job request |
US20060080292A1 (en) * | 2004-10-08 | 2006-04-13 | Alanzi Faisal Saud M | Enhanced interface utility for web-based searching |
US7707288B2 (en) | 2005-01-06 | 2010-04-27 | International Business Machines Corporation | Automatically building a locally managed virtual node grouping to handle a grid job requiring a degree of resource parallelism within a grid environment |
US7668741B2 (en) | 2005-01-06 | 2010-02-23 | International Business Machines Corporation | Managing compliance with service level agreements in a grid environment |
US20090132703A1 (en) * | 2005-01-06 | 2009-05-21 | International Business Machines Corporation | Verifying resource functionality before use by a grid job submitted to a grid environment |
US20060149576A1 (en) * | 2005-01-06 | 2006-07-06 | Ernest Leslie M | Managing compliance with service level agreements in a grid environment |
US8583650B2 (en) | 2005-01-06 | 2013-11-12 | International Business Machines Corporation | Automated management of software images for efficient resource node building within a grid environment |
US20060150190A1 (en) * | 2005-01-06 | 2006-07-06 | Gusler Carl P | Setting operation based resource utilization thresholds for resource use by a process |
US20090138594A1 (en) * | 2005-01-06 | 2009-05-28 | International Business Machines Corporation | Coordinating the monitoring, management, and prediction of unintended changes within a grid environment |
US20060150158A1 (en) * | 2005-01-06 | 2006-07-06 | Fellenstein Craig W | Facilitating overall grid environment management by monitoring and distributing grid activity |
US7743142B2 (en) * | 2005-01-06 | 2010-06-22 | International Business Machines Corporation | Verifying resource functionality before use by a grid job submitted to a grid environment |
US7761557B2 (en) | 2005-01-06 | 2010-07-20 | International Business Machines Corporation | Facilitating overall grid environment management by monitoring and distributing grid activity |
US7788375B2 (en) | 2005-01-06 | 2010-08-31 | International Business Machines Corporation | Coordinating the monitoring, management, and prediction of unintended changes within a grid environment |
US7793308B2 (en) | 2005-01-06 | 2010-09-07 | International Business Machines Corporation | Setting operation based resource utilization thresholds for resource use by a process |
US7739155B2 (en) | 2005-01-12 | 2010-06-15 | International Business Machines Corporation | Automatically distributing a bid request for a grid job to multiple grid providers and analyzing responses to select a winning grid provider |
US20080306866A1 (en) * | 2005-01-12 | 2008-12-11 | International Business Machines Corporation | Automatically distributing a bid request for a grid job to multiple grid providers and analyzing responses to select a winning grid provider |
US20080222024A1 (en) * | 2005-01-12 | 2008-09-11 | International Business Machines Corporation | Automatically distributing a bid request for a grid job to multiple grid providers and analyzing responses to select a winning grid provider |
US8346591B2 (en) | 2005-01-12 | 2013-01-01 | International Business Machines Corporation | Automating responses by grid providers to bid requests indicating criteria for a grid job |
US20080222025A1 (en) * | 2005-01-12 | 2008-09-11 | International Business Machines Corporation | Automatically distributing a bid request for a grid job to multiple grid providers and analyzing responses to select a winning grid provider |
US20080307250A1 (en) * | 2005-01-12 | 2008-12-11 | International Business Machines Corporation | Managing network errors communicated in a message transaction with error information using a troubleshooting agent |
US8396757B2 (en) | 2005-01-12 | 2013-03-12 | International Business Machines Corporation | Estimating future grid job costs by classifying grid jobs and storing results of processing grid job microcosms |
US7664844B2 (en) | 2005-01-12 | 2010-02-16 | International Business Machines Corporation | Managing network errors communicated in a message transaction with error information using a troubleshooting agent |
US20060195435A1 (en) * | 2005-02-28 | 2006-08-31 | Microsoft Corporation | System and method for providing query assistance |
US8290963B1 (en) | 2005-03-23 | 2012-10-16 | Google Inc. | Methods and systems for identifying paraphrases from an index of information items and associated sentence fragments |
US8280893B1 (en) | 2005-03-23 | 2012-10-02 | Google Inc. | Methods and systems for identifying paraphrases from an index of information items and associated sentence fragments |
US7937396B1 (en) | 2005-03-23 | 2011-05-03 | Google Inc. | Methods and systems for identifying paraphrases from an index of information items and associated sentence fragments |
US20060224554A1 (en) * | 2005-03-29 | 2006-10-05 | Bailey David R | Query revision using known highly-ranked queries |
US20060230022A1 (en) * | 2005-03-29 | 2006-10-12 | Bailey David R | Integration of multiple query revision models |
US8375049B2 (en) | 2005-03-29 | 2013-02-12 | Google Inc. | Query revision using known highly-ranked queries |
US7565345B2 (en) | 2005-03-29 | 2009-07-21 | Google Inc. | Integration of multiple query revision models |
US7870147B2 (en) | 2005-03-29 | 2011-01-11 | Google Inc. | Query revision using known highly-ranked queries |
US20110060736A1 (en) * | 2005-03-29 | 2011-03-10 | Google Inc. | Query Revision Using Known Highly-Ranked Queries |
US7617205B2 (en) | 2005-03-30 | 2009-11-10 | Google Inc. | Estimating confidence for query revision models |
US20060230005A1 (en) * | 2005-03-30 | 2006-10-12 | Bailey David R | Empirical validation of suggested alternative queries |
US20060230035A1 (en) * | 2005-03-30 | 2006-10-12 | Bailey David R | Estimating confidence for query revision models |
US9069841B1 (en) | 2005-03-30 | 2015-06-30 | Google Inc. | Estimating confidence for query revision models |
US8140524B1 (en) | 2005-03-30 | 2012-03-20 | Google Inc. | Estimating confidence for query revision models |
US7636714B1 (en) | 2005-03-31 | 2009-12-22 | Google Inc. | Determining query term synonyms within query context |
US8271453B1 (en) | 2005-09-27 | 2012-09-18 | Google Inc. | Paraphrase acquisition |
US7937265B1 (en) | 2005-09-27 | 2011-05-03 | Google Inc. | Paraphrase acquisition |
US20070100799A1 (en) * | 2005-10-31 | 2007-05-03 | Rose Daniel E | Methods for navigating collections of information in varying levels of detail |
US7693912B2 (en) * | 2005-10-31 | 2010-04-06 | Yahoo! Inc. | Methods for navigating collections of information in varying levels of detail |
US8429184B2 (en) | 2005-12-05 | 2013-04-23 | Collarity Inc. | Generation of refinement terms for search queries |
US8903810B2 (en) | 2005-12-05 | 2014-12-02 | Collarity, Inc. | Techniques for ranking search results |
US20110035403A1 (en) * | 2005-12-05 | 2011-02-10 | Emil Ismalon | Generation of refinement terms for search queries |
US8812541B2 (en) | 2005-12-05 | 2014-08-19 | Collarity, Inc. | Generation of refinement terms for search queries |
US8442972B2 (en) | 2006-10-11 | 2013-05-14 | Collarity, Inc. | Negative associations for search results ranking and refinement |
US8417721B1 (en) | 2006-10-24 | 2013-04-09 | Google Inc. | Using geographic data to identify correlated geographic synonyms |
US8326866B1 (en) | 2006-10-24 | 2012-12-04 | Google Inc. | Using geographic data to identify correlated geographic synonyms |
US8527538B1 (en) | 2006-10-24 | 2013-09-03 | Google Inc. | Using geographic data to identify correlated geographic synonyms |
US8484188B1 (en) | 2006-10-24 | 2013-07-09 | Google Inc. | Using geographic data to identify correlated geographic synonyms |
US11188544B1 (en) | 2006-11-02 | 2021-11-30 | Google Llc | Modifying search result ranking based on implicit user feedback |
US9235627B1 (en) | 2006-11-02 | 2016-01-12 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US8661029B1 (en) | 2006-11-02 | 2014-02-25 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US11816114B1 (en) | 2006-11-02 | 2023-11-14 | Google Llc | Modifying search result ranking based on implicit user feedback |
US10229166B1 (en) | 2006-11-02 | 2019-03-12 | Google Llc | Modifying search result ranking based on implicit user feedback |
US9811566B1 (en) | 2006-11-02 | 2017-11-07 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US20080215416A1 (en) * | 2007-01-31 | 2008-09-04 | Collarity, Inc. | Searchable interactive internet advertisements |
US8938463B1 (en) | 2007-03-12 | 2015-01-20 | Google Inc. | Modifying search result ranking based on implicit user feedback and a model of presentation bias |
US8694374B1 (en) | 2007-03-14 | 2014-04-08 | Google Inc. | Detecting click spam |
US9092510B1 (en) | 2007-04-30 | 2015-07-28 | Google Inc. | Modifying search result ranking based on a temporal element of user feedback |
US8694511B1 (en) | 2007-08-20 | 2014-04-08 | Google Inc. | Modifying search result ranking based on populations |
US8909655B1 (en) | 2007-10-11 | 2014-12-09 | Google Inc. | Time based ranking |
US9152678B1 (en) | 2007-10-11 | 2015-10-06 | Google Inc. | Time based ranking |
US20090157342A1 (en) * | 2007-10-29 | 2009-06-18 | China Mobile Communication Corp. Design Institute | Method and apparatus of using drive test data for propagation model calibration |
US9305113B2 (en) | 2007-12-07 | 2016-04-05 | Google Inc. | Contextual query revision |
US8996554B2 (en) | 2007-12-07 | 2015-03-31 | Google Inc. | Contextual query revision |
US8626785B2 (en) | 2007-12-07 | 2014-01-07 | Google Inc. | Contextual query revision |
US8719256B2 (en) | 2008-05-01 | 2014-05-06 | Chacha Search, Inc | Method and system for improvement of request processing |
US20090276419A1 (en) * | 2008-05-01 | 2009-11-05 | Chacha Search Inc. | Method and system for improvement of request processing |
US9128945B1 (en) | 2008-05-16 | 2015-09-08 | Google Inc. | Query augmentation |
US9916366B1 (en) | 2008-05-16 | 2018-03-13 | Google Llc | Query augmentation |
US8346791B1 (en) | 2008-05-16 | 2013-01-01 | Google Inc. | Search augmentation |
US8438178B2 (en) | 2008-06-26 | 2013-05-07 | Collarity Inc. | Interactions among online digital identities |
US20100049770A1 (en) * | 2008-06-26 | 2010-02-25 | Collarity, Inc. | Interactions among online digital identities |
US20100017392A1 (en) * | 2008-07-18 | 2010-01-21 | Jianwei Dian | Intent match search engine |
US8898152B1 (en) | 2008-12-10 | 2014-11-25 | Google Inc. | Sharing search engine relevance data |
US8396865B1 (en) | 2008-12-10 | 2013-03-12 | Google Inc. | Sharing search engine relevance data between corpora |
US9569770B1 (en) | 2009-01-13 | 2017-02-14 | Amazon Technologies, Inc. | Generating constructed phrases |
US9009146B1 (en) | 2009-04-08 | 2015-04-14 | Google Inc. | Ranking search results based on similar queries |
US8713035B2 (en) | 2009-05-18 | 2014-04-29 | Microsoft Corporation | Identifying conceptually related terms in search query results |
US8316039B2 (en) | 2009-05-18 | 2012-11-20 | Microsoft Corporation | Identifying conceptually related terms in search query results |
US20100293180A1 (en) * | 2009-05-18 | 2010-11-18 | Microsoft Corporation | Identifying conceptually related terms in search query results |
US8977612B1 (en) | 2009-07-20 | 2015-03-10 | Google Inc. | Generating a related set of documents for an initial set of documents |
US8972394B1 (en) | 2009-07-20 | 2015-03-03 | Google Inc. | Generating a related set of documents for an initial set of documents |
US9298700B1 (en) * | 2009-07-28 | 2016-03-29 | Amazon Technologies, Inc. | Determining similar phrases |
US9361362B1 (en) | 2009-08-15 | 2016-06-07 | Google Inc. | Synonym generation using online decompounding and transitivity |
US8392440B1 (en) | 2009-08-15 | 2013-03-05 | Google Inc. | Online de-compounding of query terms |
US8392441B1 (en) | 2009-08-15 | 2013-03-05 | Google Inc. | Synonym generation using online decompounding and transitivity |
US10007712B1 (en) | 2009-08-20 | 2018-06-26 | Amazon Technologies, Inc. | Enforcing user-specified rules |
US9418104B1 (en) | 2009-08-31 | 2016-08-16 | Google Inc. | Refining search results |
US8498974B1 (en) | 2009-08-31 | 2013-07-30 | Google Inc. | Refining search results |
US8738596B1 (en) | 2009-08-31 | 2014-05-27 | Google Inc. | Refining search results |
US9697259B1 (en) | 2009-08-31 | 2017-07-04 | Google Inc. | Refining search results |
US10162869B2 (en) | 2009-09-04 | 2018-12-25 | Microsoft Technology Licensing, Llc | Table of contents for search query refinement |
US8972391B1 (en) | 2009-10-02 | 2015-03-03 | Google Inc. | Recent interest based relevance scoring |
US9390143B2 (en) | 2009-10-02 | 2016-07-12 | Google Inc. | Recent interest based relevance scoring |
US8898153B1 (en) | 2009-11-20 | 2014-11-25 | Google Inc. | Modifying scoring data based on historical changes |
US8874555B1 (en) | 2009-11-20 | 2014-10-28 | Google Inc. | Modifying scoring data based on historical changes |
US20110161242A1 (en) * | 2009-12-28 | 2011-06-30 | Rovi Technologies Corporation | Systems and methods for searching and browsing media in an interactive media guidance application |
US8875038B2 (en) | 2010-01-19 | 2014-10-28 | Collarity, Inc. | Anchoring for content synchronization |
US8543381B2 (en) * | 2010-01-25 | 2013-09-24 | Holovisions LLC | Morphing text by splicing end-compatible segments |
US20110184726A1 (en) * | 2010-01-25 | 2011-07-28 | Connor Robert A | Morphing text by splicing end-compatible segments |
US8615514B1 (en) | 2010-02-03 | 2013-12-24 | Google Inc. | Evaluating website properties by partitioning user feedback |
US9485286B1 (en) | 2010-03-02 | 2016-11-01 | Amazon Technologies, Inc. | Sharing media items with pass phrases |
US8924379B1 (en) | 2010-03-05 | 2014-12-30 | Google Inc. | Temporal-based score adjustments |
US8959093B1 (en) | 2010-03-15 | 2015-02-17 | Google Inc. | Ranking search results based on anchors |
US20110295897A1 (en) * | 2010-06-01 | 2011-12-01 | Microsoft Corporation | Query correction probability based on query-correction pairs |
US20110313756A1 (en) * | 2010-06-21 | 2011-12-22 | Connor Robert A | Text sizer (TM) |
US9623119B1 (en) | 2010-06-29 | 2017-04-18 | Google Inc. | Accentuating search results |
US8832083B1 (en) | 2010-07-23 | 2014-09-09 | Google Inc. | Combining user feedback |
US11868883B1 (en) * | 2010-10-26 | 2024-01-09 | Michael Lamport Commons | Intelligent control with hierarchical stacked neural networks |
US9092479B1 (en) | 2010-11-09 | 2015-07-28 | Google Inc. | Query generation using structural similarity between documents |
US9436747B1 (en) | 2010-11-09 | 2016-09-06 | Google Inc. | Query generation using structural similarity between documents |
US8346792B1 (en) | 2010-11-09 | 2013-01-01 | Google Inc. | Query generation using structural similarity between documents |
US20120158705A1 (en) * | 2010-12-16 | 2012-06-21 | Microsoft Corporation | Local search using feature backoff |
US9002867B1 (en) | 2010-12-30 | 2015-04-07 | Google Inc. | Modifying ranking data based on document changes |
CN104584013A (en) * | 2012-08-27 | 2015-04-29 | 微软公司 | Semantic query language |
US9659082B2 (en) | 2012-08-27 | 2017-05-23 | Microsoft Technology Licensing, Llc | Semantic query language |
US10579656B2 (en) | 2012-08-27 | 2020-03-03 | Microsoft Technology Licensing, Llc | Semantic query language |
WO2014035734A1 (en) * | 2012-08-27 | 2014-03-06 | Microsoft Corporation | Semantic query language |
WO2014042556A1 (en) * | 2012-09-12 | 2014-03-20 | Ikonomov Artashes Valeryevich | System for performing a personalized information search |
US10115084B2 (en) | 2012-10-10 | 2018-10-30 | Artashes Valeryevich Ikonomov | Electronic payment system |
US9501469B2 (en) | 2012-11-21 | 2016-11-22 | University Of Massachusetts | Analogy finder |
US9183499B1 (en) | 2013-04-19 | 2015-11-10 | Google Inc. | Evaluating quality based on neighbor features |
US20150039579A1 (en) * | 2013-07-31 | 2015-02-05 | International Business Machines Corporation | Search query obfuscation via broadened subqueries and recombining |
US9721023B2 (en) * | 2013-07-31 | 2017-08-01 | International Business Machines Corporation | Search query obfuscation via broadened subqueries and recombining |
US9721020B2 (en) * | 2013-07-31 | 2017-08-01 | International Business Machines Corporation | Search query obfuscation via broadened subqueries and recombining |
US20150100564A1 (en) * | 2013-07-31 | 2015-04-09 | International Business Machines Corporation | Search query obfuscation via broadened subqueries and recombining |
US9734244B2 (en) | 2014-12-08 | 2017-08-15 | Rovi Guides, Inc. | Methods and systems for providing serendipitous recommendations |
US20180018389A1 (en) * | 2015-02-02 | 2018-01-18 | Alibaba Group Holding Limited | Method and apparatus for keyword-based text retrieval |
CN106095912A (en) * | 2016-06-08 | 2016-11-09 | 北京百度网讯科技有限公司 | For the method and apparatus generating expanding query word |
US20180365216A1 (en) * | 2017-06-20 | 2018-12-20 | The Boeing Company | Text mining a dataset of electronic documents to discover terms of interest |
US10540444B2 (en) * | 2017-06-20 | 2020-01-21 | The Boeing Company | Text mining a dataset of electronic documents to discover terms of interest |
CN108334487A (en) * | 2017-07-14 | 2018-07-27 | 腾讯科技(深圳)有限公司 | Lack semantics information complementing method, device, computer equipment and storage medium |
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AU2004262352A1 (en) | 2005-02-10 |
CN100501730C (en) | 2009-06-17 |
JP2007500903A (en) | 2007-01-18 |
RU2008101375A (en) | 2009-07-20 |
US20130159338A1 (en) | 2013-06-20 |
WO2005013153A1 (en) | 2005-02-10 |
EP1654681A1 (en) | 2006-05-10 |
CN1849603A (en) | 2006-10-18 |
CA2533605C (en) | 2014-07-08 |
AU2004262352B2 (en) | 2008-06-05 |
RU2324220C2 (en) | 2008-05-10 |
JP4731479B2 (en) | 2011-07-27 |
RU2460131C2 (en) | 2012-08-27 |
RU2006106176A (en) | 2006-06-27 |
CA2533605A1 (en) | 2005-02-10 |
US8856163B2 (en) | 2014-10-07 |
HK1092238A1 (en) | 2007-02-02 |
AU2004262352C1 (en) | 2009-01-29 |
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