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AAAI 2013 Fall Symposium on Semantics for Big Data
Entity Type Recognition for Heterogeneous Semantic Graphs
November 15, 2013
We describe an approach to reducing the computational cost of identifying coreferential instances in heterogeneous semantic graphs, where the underlying ontologies may be uninformative or even unknown. 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 varied contextual clues and require different approaches to identify potentially coreferential 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 using dictionaries derived from properties in an appropriate background knowledge base to predict instance entity types, thereby aiding coreference resolution. We evaluated the approach in experiments on data from Wikipedia, Freebase, Arnetminer, and DBpedia as the background knowledge base.
InProceedings
AAAI Press
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