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Semantically enhanced entity ranking
Abstract Users often want to find entities instead Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search.he retrieval performance of entity search.
Abstractsub Users often want to find entities instead Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search.he retrieval performance of entity search.
Bibtextype inproceedings  +
Doi 10.1007/978-3-540-85481-4_15  +
Has author Gianluca Demartini + , Firan C.S. + , Tereza Iofciu + , Wolfgang Nejdl +
Has extra keyword Dynamic programming + , Industrial engineering + , Information science + , Information systems + , Information theory + , Natural language processing systems + , Nonlinear programming + , Ontology + , Semantics + , Systems engineering + , Technology + , International conferences + , Link analysis + , Link structures + , Real-world entities + , Retrieval performance + , Semantic information + , Syntactic information + , Web information systems + , Wikipedia + , World Wide Web +
Isbn 3540854800; 9783540854807  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 176–188  +
Published in Lecture Notes in Computer Science +
Title Semantically enhanced entity ranking +
Type conference paper  +
Volume 5175 LNCS  +
Year 2008 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:49:27  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 05:49:27  +
DateThis property is a special property in this wiki. 2008  +
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