Entity Type Recognition for Heterogeneous Semantic Graphs

Authors

  • Jennifer Sleeman University of Maryland Baltimore County.
  • Tim Finin University of Maryland Baltimore County.
  • Anupam Joshi University of Maryland Baltimore County.

DOI:

https://doi.org/10.1609/aimag.v36i1.2569

Abstract

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 datas 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.
AI Magazine Spring Issue

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Published

2015-03-25

How to Cite

Sleeman, J., Finin, T., & Joshi, A. (2015). Entity Type Recognition for Heterogeneous Semantic Graphs. AI Magazine, 36(1), 75-86. https://doi.org/10.1609/aimag.v36i1.2569

Issue

Vol. 36 No. 1: Spring 2015

Section

Articles

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