CN109086440B - Knowledge extraction method and system - Google Patents
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Abstract
The invention discloses a knowledge extraction method, which comprises the following steps: s1, converting personal or organization experience into electronic intermediate knowledge through a character input technology, a voice input technology or a video input technology, and uploading the electronic intermediate knowledge to a server through a network transmission protocol; s2, classifying the intermediate knowledge according to the content structural elements contained in the intermediate knowledge; s3, extracting core knowledge points in the intermediate knowledge, and establishing a knowledge point list of the core knowledge points; and S4, structuring the content structural elements in the knowledge point list by using a knowledge extraction tool to form general knowledge, and uploading the general knowledge to a server through a network transmission protocol. The innovation points of the invention are as follows: the knowledge is classified, and the internal rules and the structural element rules among the knowledge are found according to the classification, so that the knowledge bias and the knowledge loss caused by the lack of the structure are avoided.
Description
Technical Field
The invention relates to the technical field of learning, sharing and application of network resources, in particular to a method and a system for knowledge extraction.
Background
In modern society, the internet has penetrated into people's daily lives, and more people draw external knowledge from the internet, such as learning knowledge through network electronic courses. In the network age, most knowledge is electronized into documents, audio or video information stored in a network server, and people can learn rapidly through direct browsing. However, besides the knowledge can be directly drawn through some form of information (such as word documents and videos), some implicit knowledge of organizations, personal experience and the like can be used by people. The existing typical method for converting implicit knowledge into explicit knowledge material is to compile the process of best practice experience by using a document editor (such as MS office word) and summarize the general principle, and the method for extracting knowledge has the following defects:
1. incomplete knowledge of the extraction
Writing practice cases is only suitable for knowledge including flow steps and solutions, and a large amount of knowledge of information, tools, concepts and principles cannot be extracted and refined.
2. The structure of the extraction
Different people have individual differences in understanding the elements of knowledge contained in personal experiences, resulting in poor knowledge and lack of integrity to various degrees.
3. The knowledge of the extraction is not universally applicable
The practical cases or the summarized experiences of individuals are compiled, the cognitive bias of the individuals is brought, the summarized display knowledge applicability is questionable, and the main reason is that the traditional knowledge extraction method is a simple induction method, the method is upgraded from individual experiences to general knowledge, and the verification and verification link is lacked.
Disclosure of Invention
Aiming at the problems in the prior art, the invention mainly aims to provide a knowledge extraction method, which is used for realizing the dominance and stable output of implicit knowledge.
In order to achieve the purpose, the invention provides a knowledge extraction method, which comprises the following steps:
and S1, converting the personal or organization experience into electronic intermediate knowledge through a text entry technology, a voice entry technology or a video entry technology, and uploading the electronic intermediate knowledge to a server through a network transmission protocol. Wherein the intermediate knowledge comprises a number of conditional steps generated empirically and the resulting end result.
S2, the intermediate knowledge is classified according to the content structural elements included in the intermediate knowledge. Wherein, the intermediate knowledge is divided into four categories: the system comprises information data, concepts, principle principles and process methods, wherein the information data refers to fact information which does not need to be explained, the concepts refer to categories, proper nouns, terms and various types of label information, the principle principles refer to conditions, results and relations between the conditions and the results, and the process methods refer to process step information for realizing one result.
S3, extracting core knowledge points in the intermediate knowledge, which comprises the following steps:
s31, content structural elements included in each conditional step of the intermediate knowledge and in the final result are extracted, and the content structural elements in each conditional step of the intermediate knowledge and in the final result are classified according to the classification method of the intermediate knowledge in step S2.
And S32, establishing a knowledge point list of the core knowledge points of each condition step and the final result according to the principle that the intermediate knowledge type is contained downwards.
And S4, structuring the content structural elements in the knowledge point list by using a knowledge extraction tool to form general knowledge, and uploading the general knowledge to a server through a network transmission protocol.
Preferably, in the step S31, when extracting the content structural elements included in each conditional step of the intermediate knowledge and the final result, the method further includes:
s311, according to the positive example stored in the server, comparing the content structural elements contained in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the positive example, and if the content structural elements are consistent, keeping the content structural elements consistent with the content structural elements in the positive example. If not, the content structure elements which are not consistent with the content structure elements in the regular example are deleted.
S312, comparing the content structural elements included in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the counterexample according to the counterexample stored in the server, and if not, retaining the content structural elements that are not consistent with the counterexample. If the contents match, the content structure element matching the counter example is deleted.
S313, the content structural elements retained in each conditional step and the final result of the intermediate knowledge are classified according to the method of classifying the intermediate knowledge in step S2.
Specifically, in step S32, the principle that the intermediate knowledge type is contained downward is: the process method comprises all content structural elements in the principle, the principle comprises all content structural elements in the concept, and the concept comprises all content structural elements in the information data, namely all content structural elements of the superior knowledge comprising the inferior knowledge.
Preferably, in step S4, when the knowledge extraction tool is applied, the content structure elements in the knowledge point list are structured by applying the knowledge extraction tool corresponding to the knowledge type according to the knowledge type.
Specifically, the knowledge extraction tool comprises: the system comprises an information table tool corresponding to information data, a concept combination connection tool corresponding to concepts, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to principle principles, and a flow tool, a planning tool and a monitoring tool corresponding to a process method.
The invention also provides a knowledge extraction system, comprising:
and the knowledge acquisition unit converts personal or organization experience into electronic intermediate knowledge through a character input technology, a voice input technology or a video input technology and uploads the electronic intermediate knowledge to the server through a network transmission protocol.
And a knowledge classification unit for classifying the intermediate knowledge based on the content structural elements included in the intermediate knowledge.
And the knowledge extraction unit is used for extracting content structural elements contained in each condition step of the intermediate knowledge and the final result, classifying the content structural elements through the knowledge classification unit, establishing a knowledge point list of core knowledge points of each condition step and the final result according to the principle that the intermediate knowledge type is contained downwards, and structuring the content structural elements in the knowledge point list to form general knowledge.
Preferably, the knowledge extraction unit further includes a knowledge verification unit configured to verify consistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive examples, verify inconsistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive examples, and delete the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements inconsistent with the positive examples and the content structural elements consistent with the negative examples, according to the positive examples and the negative examples in the server.
Specifically, the principle of the intermediate knowledge type contained downward is: the process method comprises all content structural elements in the principle, the principle comprises all content structural elements in the concept, and the concept comprises all content structural elements in the information data, namely all content structural elements of the superior knowledge comprising the inferior knowledge.
Specifically, be equipped with the knowledge extraction instrument corresponding with the knowledge type in the knowledge extraction unit, knowledge extraction instrument includes: the system comprises an information table tool corresponding to information data, a concept combination connection tool corresponding to concepts, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to principle principles, and a flow tool, a planning tool and a monitoring tool corresponding to a process method.
The innovation points of the knowledge extraction method and the system provided by the invention are as follows:
1. classifying the knowledge, and finding out the internal rules and structural element rules among the knowledge according to the classification, thereby avoiding the knowledge bias and knowledge loss caused by the lack of the structure;
2. the knowledge information of personal or organization experience can be rapidly and effectively extracted, the dominance and stable output of the implicit knowledge are realized, meanwhile, the extracted knowledge information is corrected, and the limitation of personal knowledge creation is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic flow chart of one embodiment of a method for knowledge extraction according to the present invention;
FIG. 2 is a schematic diagram of the extraction of content structural elements from intermediate knowledge;
FIG. 3 is a block schematic diagram of a knowledge extraction system of the present invention;
the objects, features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The invention provides a knowledge extraction method and a knowledge extraction system.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an embodiment of the method and system for extracting knowledge of the present invention, and fig. 2 is a schematic diagram of a principle of extracting content structural elements in intermediate knowledge.
As shown in fig. 1, in the embodiment of the present invention, the method of knowledge extraction includes the following steps:
and S1, converting the personal or organization experience into electronic intermediate knowledge through a text entry technology, a voice entry technology or a video entry technology, and uploading the electronic intermediate knowledge to a server through a network transmission protocol. Wherein the intermediate knowledge comprises a number of conditional steps generated empirically and the resulting end result. The condition step refers to a process step that is experimentally generated under a specific condition, and both the condition and the step include a content structural element.
S2, classifying the intermediate knowledge into four categories, i.e., information data, concepts, principle principles, and process methods, according to the content structural elements included in the intermediate knowledge. The information data refers to fact information which does not need to be explained, and the content structural elements of the information data comprise information names, 5W2H information and the like. The concept refers to category, proper noun, term and various types of label information, and the content structural elements comprise category noun, distinguishing characteristic and attribute characteristic and the like. The principle refers to information including condition factors, results and the relationship between the condition factors and the results, and formulas, laws, theorems, laws, principles, rules and the like belong to the category of classification. The content structural elements of the principle include: scenario, name of principle, a series of conditions, factors leading to a result, results produced by a condition factor, and relationship between condition factors and results. The process method refers to information of a flow step for realizing a certain result, such as a mango girdling peeling method (or process). The content structural elements of the process class information comprise: scene, name of process method, step flow, subordinate knowledge point content of step flow, result of step, and result of whole process. The principle and the scene in the process method can be the same content structural element.
S3, extracting core knowledge points in the intermediate knowledge, which comprises the following steps:
s31, content structural elements included in each conditional step of the intermediate knowledge and in the final result are extracted, and the content structural elements in each conditional step of the intermediate knowledge and in the final result are classified according to the classification method of the intermediate knowledge in step S2.
As shown in fig. 2, when extracting the content structural elements included in each conditional step of the intermediate knowledge and the final result in step S31, the method further includes:
s311, according to 1-3 positive examples stored in the server, comparing the content structural elements contained in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the positive examples, and if the content structural elements are consistent, keeping the content structural elements consistent with the content structural elements in the positive examples. If not, the content structure elements which are not consistent with the content structure elements in the regular example are deleted.
S312, according to 1-3 counter examples stored in the server, comparing the content structural elements contained in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the counter examples, if the content structural elements are not consistent, keeping the content structural elements which are not consistent with the counter examples. If the contents match, the content structure element matching the counter example is deleted.
Whether the obtained content structural elements are matched with the field of personal experience application or not is verified through 1-3 positive examples and 1-3 negative examples, so that the situation that the extracted content structural elements are irrelevant to the field and the scene is avoided, and the limitation of personal creative knowledge is reduced.
And S32, establishing a knowledge point list of the core knowledge points of each condition step and the final result according to the principle that the intermediate knowledge type is contained downwards. Specifically, the principle that the intermediate knowledge type contains downward is as follows: the process method comprises all content structural elements in the principle, the principle comprises all content structural elements in the concept, and the concept comprises all content structural elements in the information data, namely all content structural elements of the superior knowledge comprising the inferior knowledge.
And S4, structuring the content structural elements in the knowledge point list by using a knowledge extraction tool to form general knowledge, and uploading the general knowledge to a server through a network transmission protocol.
In step S4, when the knowledge extraction tool is applied, the knowledge extraction tool corresponding to the knowledge type is applied to structure the content structural elements in the knowledge point list according to the knowledge type. Specifically, the knowledge extraction tool comprises: the system comprises an information table tool corresponding to information data, a concept combination connection tool corresponding to concepts, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to principle principles, and a flow tool, a planning tool and a monitoring tool corresponding to a process method. The information form tool can be a form template, information name and 5W2H information are listed in the form template to form a visual data form or general knowledge of graphics and text description. The concept combination wiring tool is used for identifying the category of the concept and extracting category nouns, special terms and the like in the concept combination wiring tool. The listing factor tool is used for listing and measuring influence factors, the analysis decision tool is used for selecting coping strategies, the matrix strategy tool is used for listing scenes and scene coping strategies, and therefore various content structural elements in principle principles are structured, a practical application case or a variable reference case is formed and stored in the server, and the comparison file is convenient for a user to learn or is used as a comparison file for extracting knowledge in personal experience in the same field. The process tool can draw the existing process, the planning tool can draw the planned process, and the monitoring tool is used for setting monitoring points in the process, so that the content structural elements in the process method are structured to form a practice case or a demonstration process.
According to the technical scheme, the knowledge is classified, and the intrinsic rules and the structural element rules among the knowledge are found according to the classification, so that the knowledge bias and knowledge loss caused by the lack of the structure are avoided, and the experience is effectively converted into general explicit knowledge information. Meanwhile, the extracted general knowledge information is corrected through the practice case, and the limitation of creating knowledge by individuals is avoided.
In addition, the invention also provides a knowledge extraction system.
As shown in fig. 3, the system includes a knowledge acquisition unit 100, a knowledge classification unit 200, and a knowledge extraction unit 300. The knowledge acquisition unit 100 converts personal or organizational experiences into electronic intermediate knowledge through a text entry technology, a voice entry technology or a video entry technology, and uploads the intermediate knowledge to the server through a network transmission protocol. The knowledge classification unit 200 may classify the intermediate knowledge based on the content structural elements included in the intermediate knowledge. The knowledge extraction unit 300 is configured to extract content structural elements included in each condition step of the intermediate knowledge and the final result, classify the content structural elements by the knowledge classification unit 200, establish a knowledge point list of core knowledge points of each condition step and the final result according to a principle that the intermediate knowledge type is included downwards, and structure the content structural elements in the knowledge point list to form general knowledge. In this embodiment, the knowledge acquisition unit 100, the knowledge classification unit 200, and the knowledge extraction unit 300 may be existing application level clients, web pages, or the like. The network transmission protocol refers to computer communication language and is a network communication technology between an application and a server.
In this embodiment, the principle that the intermediate knowledge type is contained downward is: the process method comprises all content structural elements in the principle, the principle comprises all content structural elements in the concept, and the concept comprises all content structural elements in the information data, namely all content structural elements of the superior knowledge comprising the inferior knowledge.
In this embodiment, the knowledge extracting unit 300 further includes a knowledge verifying unit configured to verify consistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive example, verify inconsistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive example, and delete the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements inconsistent with the positive example and the content structural elements consistent with the negative example, according to the positive example and the negative example in the server.
In this embodiment, a knowledge extraction tool corresponding to the knowledge type is provided in the knowledge extraction unit 300, and the knowledge extraction tool includes: the system comprises an information table tool corresponding to information data, a concept combination connection tool corresponding to concepts, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to principle principles, and a flow tool, a planning tool and a monitoring tool corresponding to a process method.
The knowledge extraction system provided by the invention can rapidly acquire and find the internal rules and the structural element rules among the knowledge by classifying the knowledge, thereby avoiding the knowledge bias and the knowledge loss caused by the lack of the structure. Meanwhile, the system can also realize the rapid and effective extraction of general knowledge information in personal or organization experience, realize the dominance and stable output of implicit knowledge, correct the extracted knowledge information and avoid the limitation of personal knowledge creation.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A method of knowledge extraction, comprising the steps of:
s1, converting personal or organization experience into electronic intermediate knowledge through a character input technology, a voice input technology or a video input technology, and uploading the electronic intermediate knowledge to a server through a network transmission protocol; wherein the intermediate knowledge comprises a plurality of empirically generated conditional steps and a generated final result;
s2, classifying the intermediate knowledge according to the content structural elements contained in the intermediate knowledge; wherein the intermediate knowledge is divided into four classes: the system comprises information data, concepts, principle principles and a process method, wherein the information data refers to fact information which does not need to be explained, the concepts refer to categories, proper nouns, terms and various types of label information, the principle principles refer to conditions, results and relations between the conditions and the results, and the process method refers to process step information for realizing one result;
s3, extracting core knowledge points in the intermediate knowledge, which comprises the following steps:
s31, extracting the content structural elements included in each conditional step of the intermediate knowledge and the final result, and classifying the content structural elements in each conditional step of the intermediate knowledge and the final result according to the classification method of the intermediate knowledge in the step S2;
s32, establishing a knowledge point list of the core knowledge points of each condition step and the final result according to the principle that the intermediate knowledge type contains downwards;
s4, structuring the content structural elements in the knowledge point list by using a knowledge extraction tool to form general knowledge, and uploading the general knowledge to a server through a network transmission protocol; wherein the knowledge extraction tool comprises: an information table tool corresponding to the information data, a concept combination connection tool corresponding to the concept, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to the principle, and a flow tool, a planning tool and a monitoring tool corresponding to the process method.
2. The method of extracting knowledge according to claim 1, wherein the step S31, when extracting the content structural elements included in each conditional step of the intermediate knowledge and the final result, further includes:
s311, comparing the content structural elements contained in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the positive example according to the positive example stored in the server, and if the content structural elements are consistent, keeping the content structural elements consistent with the content structural elements in the positive example; if not, deleting the content structural elements which are not consistent with the content structural elements in the regular example;
s312, comparing the content structural elements contained in each condition step of extracting the intermediate knowledge and the final result with the content structural elements in the counterexample according to the counterexample stored in the server, and if the content structural elements are not consistent with the content structural elements in the counterexample, keeping the content structural elements inconsistent with the counterexample; if the two contents are consistent, deleting the content structural elements consistent with the opposite example;
s313, the content structural elements retained in each conditional step and the final result of the intermediate knowledge are classified according to the method of classifying the intermediate knowledge in step S2.
3. The method for extracting knowledge as claimed in claim 1, wherein in the step S32, the principle that the intermediate knowledge type is contained downwards is as follows: the process method comprises all content structural elements in a principle, wherein the principle comprises all content structural elements in a concept, and the concept comprises all content structural elements in information data, namely all content structural elements of superior knowledge comprising subordinate knowledge.
4. The method of knowledge extraction according to claim 1, wherein in step S4, when the knowledge extraction tool is applied, the knowledge extraction tool corresponding to the knowledge type is applied to structure the content structural elements in the knowledge point list according to the knowledge type.
5. A knowledge extraction system, comprising:
the knowledge acquisition unit converts personal or organization experience into electronic intermediate knowledge through a character input technology, a voice input technology or a video input technology and uploads the electronic intermediate knowledge to the server through a network transmission protocol;
a knowledge classification unit that classifies the intermediate knowledge based on content structural elements included in the intermediate knowledge; wherein, the intermediate knowledge is divided into four categories: the system comprises information data, concepts, principle principles and a process method, wherein the information data refers to fact information which does not need to be explained, the concepts refer to categories, proper nouns, terms and various types of label information, the principle principles refer to conditions, results and the relationship between the conditions and the results, and the process method refers to process step information for realizing one result;
the knowledge extraction unit is used for extracting content structural elements contained in each condition step of the intermediate knowledge and the final result, classifying the content structural elements through the knowledge classification unit, establishing a knowledge point list of core knowledge points of each condition step and the final result according to the principle that the intermediate knowledge type is contained downwards, and structuring the content structural elements in the knowledge point list to form general knowledge; wherein, be equipped with the knowledge extraction instrument corresponding with the knowledge type in the knowledge extraction unit, the knowledge extraction instrument includes: an information table tool corresponding to the information data, a concept combination connection tool corresponding to the concept, a listing factor tool, an analysis decision tool and a matrix strategy tool corresponding to the principle, and a flow tool, a planning tool and a monitoring tool corresponding to the process method.
6. The knowledge extraction system according to claim 5, wherein the knowledge extraction unit further comprises a knowledge verification unit operable to verify, based on the positive case and the negative case in the server, consistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive case, verify inconsistency of the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements in the positive case, and delete the content structural elements included in each condition step of the intermediate knowledge and the final result with the content structural elements inconsistent with the positive case and the content structural elements consistent with the negative case.
7. The knowledge extraction system of claim 5, wherein the intermediate knowledge types include down the principle of: the process method comprises all content structural elements in a principle, wherein the principle comprises all content structural elements in a concept, and the concept comprises all content structural elements in information data, namely all content structural elements of superior knowledge comprising subordinate knowledge.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005078240A (en) * | 2003-08-29 | 2005-03-24 | Mamoru Tanaka | Method for extracting knowledge by data mining |
| CN101295378A (en) * | 2008-06-12 | 2008-10-29 | 宁波钢铁有限公司 | Execution management system and implicit knowledge explicating method |
| CN102779143A (en) * | 2012-01-31 | 2012-11-14 | 中国科学院自动化研究所 | Visualizing method for knowledge genealogy |
| CN103425774A (en) * | 2013-08-13 | 2013-12-04 | 北京航空航天大学 | Tacit knowledge acquisition method based on HWME (Hall for Workshop of Metasynthetic Engineering) |
| CN106203632A (en) * | 2016-07-12 | 2016-12-07 | 中国科学院科技政策与管理科学研究所 | A kind of limited knowledge collection recombinant is also distributed the study of extraction and application system method |
| CN107506612A (en) * | 2017-10-18 | 2017-12-22 | 安吉县新星文化培训学校 | A kind of dynamic digital publishing system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7930256B2 (en) * | 2006-05-23 | 2011-04-19 | Charles River Analytics, Inc. | Security system for and method of detecting and responding to cyber attacks on large network systems |
-
2018
- 2018-08-15 CN CN201810931119.9A patent/CN109086440B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005078240A (en) * | 2003-08-29 | 2005-03-24 | Mamoru Tanaka | Method for extracting knowledge by data mining |
| CN101295378A (en) * | 2008-06-12 | 2008-10-29 | 宁波钢铁有限公司 | Execution management system and implicit knowledge explicating method |
| CN102779143A (en) * | 2012-01-31 | 2012-11-14 | 中国科学院自动化研究所 | Visualizing method for knowledge genealogy |
| CN103425774A (en) * | 2013-08-13 | 2013-12-04 | 北京航空航天大学 | Tacit knowledge acquisition method based on HWME (Hall for Workshop of Metasynthetic Engineering) |
| CN106203632A (en) * | 2016-07-12 | 2016-12-07 | 中国科学院科技政策与管理科学研究所 | A kind of limited knowledge collection recombinant is also distributed the study of extraction and application system method |
| CN107506612A (en) * | 2017-10-18 | 2017-12-22 | 安吉县新星文化培训学校 | A kind of dynamic digital publishing system |
Non-Patent Citations (2)
| Title |
|---|
| Using Publication Metrics to Highlight Academic Productivity and Research Impact;Christopher R. Carpenter等;《HHS Public Access Author manuscript》;20160817;1 * |
| 论课程知识的社会建构;李殿森;《中国优秀博硕士学位论文全文数据库（博士） 社会科学II辑》;20061115;H127-14 * |
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