Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96180
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dc.contributor.authorChua, Watson Wei Khongen
dc.contributor.authorKim, Jung-jaeen
dc.date.accessioned2013-07-22T02:47:27Zen
dc.date.accessioned2019-12-06T19:26:39Z-
dc.date.available2013-07-22T02:47:27Zen
dc.date.available2019-12-06T19:26:39Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationChua, W. W. K., & Kim, J.-j. (2012). Semantic querying over knowledge in biomedical text corpora annotated with multiple ontologies. Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12.en
dc.identifier.urihttps://hdl.handle.net/10356/96180-
dc.identifier.urihttp://hdl.handle.net/10220/11920en
dc.description.abstractExisting ontology-based knowledge representations systems have achieved considerable success in semantic querying on large biomedical text corpora over keyword-based systems. However, their query expressivity is limited due to the lack of cross-ontology integration and semantic relations. We present a System for Multiple-Ontology Knowledge Representation (SMOKR) to alleviate the problem. The system first performs annotations of phrases and the semantic relations between them using different domain ontologies, before instantiating the ontologies with the annotated phrases. It then integrates the ontologies by matching their instances using simple NLP techniques, and also by matching their concepts using the state-of-the-art Biomedical Ontology Alignment Tool (BOAT). SMOKR performs inconsistency detection to remove conflicting axioms in order to create a consistent ontology for querying. We evaluate the performance of the system by testing it with a set of semantic queries, and the results are compared to a keyword-based search engine, Lucene, and a hybrid system, SSOKR_Luc, which combines a knowledge representation system using a single ontology and the keyword-based search engine, Lucene. SMOKR shows the best performance of F-Measures 0.7 and 0.87 on the GRO Corpus and the GENIA Corpus, respectively, compared to that of SSOKR_Luc at 0.62 and 0.33, and that of Lucene at 0.36 and 0.12.en
dc.language.isoenen
dc.rights© 2012 AMC.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleSemantic querying over knowledge in biomedical text corpora annotated with multiple ontologiesen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceConference on Bioinformatics, Computational Biology and Biomedicine (2012 : Orlando, USA)en
dc.identifier.doi10.1145/2382936.2382987en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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