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, the authors show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. They demonstrate the power of their method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL (Never-Ending Language Learner) project. NOTE: Also available as a PDF:

Keywords: Probabilistic SoftLogic (PSL), Ontology, Named entity extraction, Knowledge graph
Author: Pujara, Jay
Date created: 2013-11-28 05:00:00.000
Time required: P15M
Educational use: instruction
Educational audience: generalPublic
Interactivity type: expositive

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