Carnegie Mellon University
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Reading The Web with Learned Syntactic-Semantic Inference Rules

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journal contribution
posted on 2012-07-01, 00:00 authored by Ni Lao, Amarnag Subramanya, Fernando Pereira, William W. Cohen

We study how to extend a large knowledge base (Freebase) by reading relational information from a large Web text corpus. Previous studies on extracting relational knowledge from text show the potential of syntactic patterns for extraction, but they do not exploit background knowledge of other relations in the knowledge base. We describe a distributed, Web-scale implementation of a path-constrained random walk model that learns syntactic-semantic inference rules for binary relations from a graph representation of the parsed text and the knowledge base. Experiments show significant accuracy improvements in binary relation prediction over methods that consider only text, or only the existing knowledge base.




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