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Large-Scale Knowledge Graph Identification using PSL

By Jay Pujara, Hui Miao, Lise Getoor and William Cohen

Abstract

Building a web-scale knowledge graph, which captures information about entities and the relationships between them, represents a formidable challenge. While many largescale information extraction systems operate on web corpora, the candidate facts they produce are noisy and incomplete. To remove noise and infer missing information in the knowledge graph, we propose knowledge graph identification: a process of jointly reasoning about the structure of the knowledge graph, utilizing extraction confidences and leveraging ontological information. Scalability is often a challenge when building models in domains with rich structure, but we use probabilistic soft logic (PSL), a recentlyintroduced probabilistic modeling framework which easily scales to millions of facts. In practice, our method performs joint inference on a real-world dataset containing over 1M facts and 80K ontological constraints in 12 hours and produces a high-precision set of facts for inclusion into a knowledge graph. 1

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.9253
Provided by: CiteSeerX
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