Discovering latent concepts and exploiting ontological features for semantic text search
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|Title:||Discovering latent concepts and exploiting ontological features for semantic text search||Authors:||Ngo, Vuong M.||Permanent link:||http://hdl.handle.net/10197/11808||Date:||13-Nov-2011||Online since:||2020-12-11T10:57:55Z||Abstract:||Named entities and WordNet words are important in defining the content of a text in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. WordNet words also have ontological features, namely, their synonyms, hypernyms, hyponyms, and senses. Those features of concepts may be hidden from their textual appearance. Besides, there are related concepts that do not appear in a query, but can bring out the meaning of the query if they are added. The traditional constrained spreading activation algorithms use all relations of a node in the network that will add unsuitable information into the query. Meanwhile, we only use relations represented in the query. We propose an ontology-based generalized Vector Space Model to semantic text search. It discovers relevant latent concepts in a query by relation constrained spreading activation. Besides, to represent a word having more than one possible direct sense, it combines the most specific common hypernym of the remaining undisambiguated multi-senses with the form of the word. Experiments on a benchmark dataset in terms of the MAP measure for the retrieval performance show that our model is 41.9% and 29.3% better than the purely keyword-based model and the traditional constrained spreading activation model, respectively||Type of material:||Conference Publication||Publisher:||Asian Federation of Natural Language Processing||Copyright (published version):||2011 AFNLP||Keywords:||Named entities; WordNet words; Information retrieval; Latent concepts; Ontology-based text Search||Other versions:||https://www.aclweb.org/portal/content/ijcnlp-2011-5th-international-joint-conference-natural-language-processing||Language:||en||Status of Item:||Peer reviewed||Is part of:||Wang, H. Yarowsky, D. Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 571–579||Conference Details:||The 5th International Joint Conference on Natural Language Processing (IJCNLP 2011), Chiang Mai, Thailand, 8-13 November 2011||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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