Publication | Closed Access
An effective approach to entity resolution problem using quasi-clique and its application to digital libraries
23
Citations
4
References
2006
Year
Unknown Venue
EngineeringKnowledge ExtractionEntity SummarizationDigital LibrariesSemantic WebCorpus LinguisticsText MiningNatural Language ProcessingEffective ApproachInformation RetrievalData ScienceComputational LinguisticsRelated ElementsManagementData IntegrationSemi-structured DataQuery ExpansionNamed-entity RecognitionData ManagementAuthor EntityEntity Resolution SolutionsEntity DisambiguationKnowledge DiscoveryComputer ScienceDatabase TheoryKnowledge BaseRelationship ExtractionEntity Resolution Problem
We study how to resolve entities that contain a group of related elements in them (e.g., an author entity with a list of citations or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. By exploiting contextual information mined from the group of elements per entity in addition to syntactic similarity, we show that our approach, Quasi-Clique, improves precision and recall unto 91% when used together with a variety of existing entity resolution solutions, but never worsens them.
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