2013 KnowledgeGraphIdentification

• (Pujara et al., 2013) ⇒ Jay Pujara, Hui Miao, Lise Getoor, and William Cohen. (2013). “Knowledge Graph Identification.” In: Proceedings of the 12th International Semantic Web Conference - Part I. ISBN:978-3-642-41334-6 doi:10.1007/978-3-642-41335-3_34

Subject Headings: Knowledge Graph Extraction.

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Knowledge+Graph+Identification
• http://dl.acm.org/citation.cfm?id=2717129.2717164&preflayout=flat#citedby

Quotes

Abstract

Large-scale information processing systems are able to extract massive collections of interrelated facts, but unfortunately transforming these candidate facts into useful knowledge is a formidable challenge. In this paper, we show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. The extractions form an extraction graph and we refer to the task of removing noise, inferring missing information, and determining which candidate facts should be included into a knowledge graph as knowledge graph identification. In order to perform this task, we must reason jointly about candidate facts and their associated extraction confidences, identify co-referent entities, and incorporate ontological constraints. Our proposed approach uses probabilistic soft logic (PSL), a recently introduced probabilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL project containing over 1M extractions and 70K ontological relations. We show that compared to existing methods, our approach is able to achieve improved AUC and F1 with significantly lower running time.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 KnowledgeGraphIdentification	William W. Cohen Jay Pujara Hui Miao Lise Getoor			Knowledge Graph Identification				10.1007/978-3-642-41335-3_34		2013