Entity Type Recognition for Heterogeneous Semantic Graphs

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dc.contributor.authorSleeman, Jennifer
dc.contributor.authorFinin, Tim
dc.date.accessioned2018-11-02T15:35:12Z
dc.date.available2018-11-02T15:35:12Z
dc.date.issued2013-11
dc.descriptionAAAI 2013 Fall Symposium on Semantics for Big Dataen_US
dc.description.abstractWe describe an approach to reducing the computational cost of identifying coreferent instances in heterogeneous semantic graphs where the underlying ontologies may not be informative or even known. The problem is similar to coreference resolution in unstructured text, where a variety of linguistic clues and contextual information is used to infer entity types and predict coreference. Semantic graphs, whether in RDF or another formalism, are semi-structured data with very different contextual clues and need different approaches to identify potentially coreferent entities. When their ontologies are unknown, inaccessible or semantically trivial, coreference resolution is difficult. For such cases, we can use supervised machine learning to map entity attributes via dictionaries based on properties from an appropriate background knowledge base to predict instance entity types, aiding coreference resolution. We evaluated the approach in experiments on data from Wikipedia, Freebase and Arnetminer and DBpedia as the background knowledge base.en_US
dc.description.urihttps://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2569en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierdoi:10.13016/M2Q23R41C
dc.identifier.citationJennifer Sleeman and Tim Finin, Recognizing Entity Types in Heterogeneous Semantic Graphs, AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington VA, Nov. 2013, https://www.aaai.org/ocs/index.php/FSS/FSS13/paper/viewFile/7617/7555en_US
dc.identifier.urihttp://hdl.handle.net/11603/11845
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.subjectEntityen_US
dc.subjectRecognitionen_US
dc.subjectHeterogeneousen_US
dc.subjectSemantic Graphsen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleEntity Type Recognition for Heterogeneous Semantic Graphsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-8934-5587
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792
dcterms.creatorhttps://orcid.org/0000-0002-8641-3193

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