Semantic Search by Latent Ontological Features
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|Title:||Semantic Search by Latent Ontological Features||Authors:||Cao, Tru H.; Ngo, Vuong M.||Permanent link:||http://hdl.handle.net/10197/11806||Date:||7-Feb-2012||Online since:||2020-12-11T10:50:25Z||Abstract:||Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.||Type of material:||Journal Article||Publisher:||Springer||Journal:||New Generation Computing||Volume:||30||Issue:||1||Start page:||53||End page:||71||Copyright (published version):||2012 Ohmsha Ltd. and Springer||Keywords:||Named entities; Ontology; Semantic annotation; Vector space model; Information retrieval||DOI:||10.1007/s00354-012-0104-0||Language:||en||Status of Item:||Peer reviewed||ISSN:||0288-3635||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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