KELVIN: Extracting Knowledge from Large Text Collections

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dc.contributor.authorMayfield, James
dc.contributor.authorMcNamee, Paul
dc.contributor.authorHarmon, Craig
dc.contributor.authorFinin, Tim
dc.contributor.authorLawrie, Dawn
dc.date.accessioned2018-11-01T16:36:26Z
dc.date.available2018-11-01T16:36:26Z
dc.date.issued2014-11-13
dc.descriptionAAAI Fall Symposium on Natural Language Access to Big Dataen_US
dc.description.abstractWe describe the KELVIN system for extracting entities and relations from large text collections and its use in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and Technology. The Cold Start task starts with an empty knowledge base defined by an ontology or entity types, properties and relations. Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to de-emphasize the linking entities to a background knowledge bases such as Wikipedia. Interesting features of KELVIN include a cross-document entity coreference module based on entity mentions, removal of suspect intra-document conference chains, a slot value consolidator for entities, the application of inference rules to expand the number of asserted facts and a set of analysis and browsing tools supporting development.en_US
dc.description.sponsorshipPartial support for this work was provided by NSF grants 0910838 and 1228673.en_US
dc.description.urihttps://www.aaai.org/ocs/index.php/FSS/FSS14/paper/download/9140/9083en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierdoi:10.13016/M2901ZK5F
dc.identifier.citationMayfield, James, Paul McNamee, Craig Harman, Tim Finin, and Dawn Lawrie. “KELVIN: Extracting Knowledge from Large Text Collections.” Natural Language Access to Big Data: Papers from the 2014 AAAI Fall Symposium. AAAI Press (2022). https://aaai.org/papers/09140-kelvin-extracting-knowledge-from-large-text-collections/.en_US
dc.identifier.urihttp://hdl.handle.net/11603/11826
dc.language.isoen_USen_US
dc.publisherAAAI Pressen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectKnowledgeen_US
dc.subjectLarge Text Collectionsen_US
dc.subjectKELVINen_US
dc.subjectKnowledge Base Populationen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleKELVIN: Extracting Knowledge from Large Text Collectionsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792

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