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Entity Type Recognition for Heterogeneous Semantic Graphs
Author(s) -
Sleeman Jennifer,
Finin Tim,
Joshi Anupam
Publication year - 2015
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v36i1.2569
Subject(s) - coreference , computer science , entity linking , knowledge graph , information retrieval , knowledge base , natural language processing , linked data , artificial intelligence , set (abstract data type) , semantic web , resolution (logic) , programming language
We describe an approach for identifying fine‐grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine‐grained entity types, rather than a few high‐level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the data's ontologies are unknown, inaccessible, or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

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