Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017
Faegheh Hasibi (1) Dar\'io Garigliotti (2), Shuo Zhang (2), Krisztian, Balog (2) ((1) NTNU Trondheim, (2) University of Stavanger)
TL;DR
This paper presents a supervised ranking approach for triples in knowledge bases, focusing on profession and nationality relations, achieving top performance in the WSDM Cup 2017.
Contribution
The authors introduce a novel supervised ranking method with custom features for triple scoring in knowledge bases.
Findings
Top-ranked in average score difference
Second best in Kendall's tau
Effective supervised ranking approach
Abstract
This paper describes our participation in the Triple Scoring task of WSDM Cup 2017, which aims at ranking triples from a knowledge base for two type-like relations: profession and nationality. We introduce a supervised ranking method along with the features we designed for this task. Our system has been top ranked with respect to average score difference and 2nd best in terms of Kendall's tau.
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Taxonomy
TopicsData Mining Algorithms and Applications · Advanced Clustering Algorithms Research · Bayesian Modeling and Causal Inference