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dc.contributor.authorMedelyan, Olena
dc.contributor.authorManion, Steve
dc.contributor.authorBroekstra, Jeen
dc.contributor.authorDivoli, Anna
dc.contributor.authorHuang, Anna-Lan
dc.contributor.authorWitten, Ian H.
dc.coverage.spatialConference held at Montpellier, Franceen_NZ
dc.date.accessioned2013-09-06T05:02:14Z
dc.date.available2013-09-06T05:02:14Z
dc.date.copyright2013
dc.date.issued2013
dc.identifier.citationMedelyan, O., Manion, S., Broekstra, J., Divoli, A., Huang, A.-L., & Witten, I. H. (2013). Constructing a focused taxonomy from a document collection. In P. Cimiano et al. (Eds.): ESWC 2013, LNCS 7882 (pp. 367-381). Berlin, Germany: Springer-Verlag Berlin Heidelberg.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/7975
dc.description.abstractWe describe a new method for constructing custom taxonomies from document collections. It involves identifying relevant concepts and entities in text; linking them to knowledge sources like Wikipedia, DBpedia, Freebase, and any supplied taxonomies from related domains; disambiguating conflicting concept mappings; and selecting semantic relations that best group them hierarchically. An RDF model supports interoperability of these steps, and also provides a flexible way of including existing NLP tools and further knowledge sources. From 2000 news articles we construct a custom taxonomy with 10,000 concepts and 12,700 relations, similar in structure to manually created counterparts. Evaluation by 15 human judges shows the precision to be 89% and 90% for concepts and relations respectively; recall was 75% with respect to a manually generated taxonomy for the same domain.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherSpringer-Verlag Berlin Heidelbergen_NZ
dc.relation.urihttp://link.springer.com/chapter/10.1007%2F978-3-642-38288-8_25
dc.rightsThis is an author’s accepted version. The original publication is available at www.springerlink.com.en_NZ
dc.sourceESWC 2013en_NZ
dc.subjectcomputer scienceen_NZ
dc.titleConstructing a focused taxonomy from a document collectionen_NZ
dc.typeConference Contributionen_NZ
dc.identifier.doi10.1007/978-3-642-38288-8_25en_NZ
dc.relation.isPartOfProc 10th International Conference on the Semantic Web: Semantics and Big Dataen_NZ
pubs.begin-page367en_NZ
pubs.elements-id22944
pubs.end-page381en_NZ
pubs.finish-date2013-05-30en_NZ
pubs.start-date2013-05-26en_NZ
pubs.volumeLNCS 7882en_NZ


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