Exploiting Locality of Wikipedia Links in Entity Ranking
Identifieur interne : 008824 ( Main/Curation ); précédent : 008823; suivant : 008825Exploiting Locality of Wikipedia Links in Entity Ranking
Auteurs : Jovan Pehcevski [France] ; Anne-Marie Vercoustre [France] ; James A. Thom [Australie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Descripteurs français
- Wicri :
- topic : Euro, Conférence internationale, Moteur de recherche.
English descriptors
- KwdEn :
- Algorithm, Anchor text, Broad context, Category score, Category similarity score, Consistent performance improvement, Czech republic, Document collection, Dynamic contexts, Entity, Entity disambiguation, Entity example, Entity examples, Entity page, Entity recognition, Euro, Euro page, European countries, Evaluation measures, External knowledge, Full page, Full page context, Fullpage statl statr dyncre, Global score, Good entity page, Inex, Information retrieval, Initial zettair score, International conference, Joint conference, Linear combination, Link analysis, Linkrank, Linkrank function, Linkrank module, Linkrank score, Links, Module, Narrow contexts, Open source search engines, Optimal value, Optimal values, Pehcevski, Performance scores, Positive impact, Query, Ranking entities, Relevance assessments, Relevant entities, Retrieval, Retrieving entities, Rmit university, Search engine, Second task, Static contexts, Target entities, Target entity, Target entity page, Test collection, Topic title, Vercoustre, Wikipedia, Wikipedia categories, Wikipedia links, Wikipedia page, Wikipedia pages, Zettair.
- Teeft :
- Algorithm, Anchor text, Broad context, Category score, Category similarity score, Consistent performance improvement, Czech republic, Document collection, Dynamic contexts, Entity, Entity disambiguation, Entity example, Entity examples, Entity page, Entity recognition, Euro, Euro page, European countries, Evaluation measures, External knowledge, Full page, Full page context, Fullpage statl statr dyncre, Global score, Good entity page, Inex, Information retrieval, Initial zettair score, International conference, Joint conference, Linear combination, Link analysis, Linkrank, Linkrank function, Linkrank module, Linkrank score, Links, Module, Narrow contexts, Open source search engines, Optimal value, Optimal values, Pehcevski, Performance scores, Positive impact, Query, Ranking entities, Relevance assessments, Relevant entities, Retrieval, Retrieving entities, Rmit university, Search engine, Second task, Static contexts, Target entities, Target entity, Target entity page, Test collection, Topic title, Vercoustre, Wikipedia, Wikipedia categories, Wikipedia links, Wikipedia page, Wikipedia pages, Zettair.
Abstract
Abstract: Information retrieval from web and XML document collections is ever more focused on returning entities instead of web pages or XML elements. There are many research fields involving named entities; one such field is known as entity ranking, where one goal is to rank entities in response to a query supported with a short list of entity examples. In this paper, we describe our approach to ranking entities from the Wikipedia XML document collection. Our approach utilises the known categories and the link structure of Wikipedia, and more importantly, exploits link co-occurrences to improve the effectiveness of entity ranking. Using the broad context of a full Wikipedia page as a baseline, we evaluate two different algorithms for identifying narrow contexts around the entity examples: one that uses predefined types of elements such as paragraphs, lists and tables; and another that dynamically identifies the contexts by utilising the underlying XML document structure. Our experiments demonstrate that the locality of Wikipedia links can be exploited to significantly improve the effectiveness of entity ranking.
Url:
DOI: 10.1007/978-3-540-78646-7_25
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Thom</name><affiliation wicri:level="3"><country xml:lang="fr">Australie</country><wicri:regionArea>RMIT University, Melbourne</wicri:regionArea><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Australie</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2008</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithm</term><term>Anchor text</term><term>Broad context</term><term>Category score</term><term>Category similarity score</term><term>Consistent performance improvement</term><term>Czech republic</term><term>Document collection</term><term>Dynamic contexts</term><term>Entity</term><term>Entity disambiguation</term><term>Entity example</term><term>Entity examples</term><term>Entity page</term><term>Entity recognition</term><term>Euro</term><term>Euro page</term><term>European countries</term><term>Evaluation measures</term><term>External knowledge</term><term>Full page</term><term>Full page context</term><term>Fullpage statl statr dyncre</term><term>Global score</term><term>Good entity page</term><term>Inex</term><term>Information retrieval</term><term>Initial zettair score</term><term>International conference</term><term>Joint conference</term><term>Linear combination</term><term>Link analysis</term><term>Linkrank</term><term>Linkrank function</term><term>Linkrank module</term><term>Linkrank score</term><term>Links</term><term>Module</term><term>Narrow contexts</term><term>Open source search engines</term><term>Optimal value</term><term>Optimal values</term><term>Pehcevski</term><term>Performance scores</term><term>Positive impact</term><term>Query</term><term>Ranking entities</term><term>Relevance assessments</term><term>Relevant entities</term><term>Retrieval</term><term>Retrieving entities</term><term>Rmit university</term><term>Search engine</term><term>Second task</term><term>Static contexts</term><term>Target entities</term><term>Target entity</term><term>Target entity page</term><term>Test collection</term><term>Topic title</term><term>Vercoustre</term><term>Wikipedia</term><term>Wikipedia categories</term><term>Wikipedia links</term><term>Wikipedia page</term><term>Wikipedia pages</term><term>Zettair</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Algorithm</term><term>Anchor text</term><term>Broad context</term><term>Category score</term><term>Category similarity score</term><term>Consistent performance improvement</term><term>Czech republic</term><term>Document collection</term><term>Dynamic contexts</term><term>Entity</term><term>Entity disambiguation</term><term>Entity example</term><term>Entity examples</term><term>Entity page</term><term>Entity recognition</term><term>Euro</term><term>Euro page</term><term>European countries</term><term>Evaluation measures</term><term>External knowledge</term><term>Full page</term><term>Full page context</term><term>Fullpage statl statr dyncre</term><term>Global score</term><term>Good entity page</term><term>Inex</term><term>Information retrieval</term><term>Initial zettair score</term><term>International conference</term><term>Joint conference</term><term>Linear combination</term><term>Link analysis</term><term>Linkrank</term><term>Linkrank function</term><term>Linkrank module</term><term>Linkrank score</term><term>Links</term><term>Module</term><term>Narrow contexts</term><term>Open source search engines</term><term>Optimal value</term><term>Optimal values</term><term>Pehcevski</term><term>Performance scores</term><term>Positive impact</term><term>Query</term><term>Ranking entities</term><term>Relevance assessments</term><term>Relevant entities</term><term>Retrieval</term><term>Retrieving entities</term><term>Rmit university</term><term>Search engine</term><term>Second task</term><term>Static contexts</term><term>Target entities</term><term>Target entity</term><term>Target entity page</term><term>Test collection</term><term>Topic title</term><term>Vercoustre</term><term>Wikipedia</term><term>Wikipedia categories</term><term>Wikipedia links</term><term>Wikipedia page</term><term>Wikipedia pages</term><term>Zettair</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Euro</term><term>Conférence internationale</term><term>Moteur de recherche</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Information retrieval from web and XML document collections is ever more focused on returning entities instead of web pages or XML elements. There are many research fields involving named entities; one such field is known as entity ranking, where one goal is to rank entities in response to a query supported with a short list of entity examples. In this paper, we describe our approach to ranking entities from the Wikipedia XML document collection. Our approach utilises the known categories and the link structure of Wikipedia, and more importantly, exploits link co-occurrences to improve the effectiveness of entity ranking. Using the broad context of a full Wikipedia page as a baseline, we evaluate two different algorithms for identifying narrow contexts around the entity examples: one that uses predefined types of elements such as paragraphs, lists and tables; and another that dynamically identifies the contexts by utilising the underlying XML document structure. Our experiments demonstrate that the locality of Wikipedia links can be exploited to significantly improve the effectiveness of entity ranking.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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