Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017
This work addresses ranking accuracy for knowledge base triples, but it is incremental as it applies supervised methods to a specific competition task.
The paper tackled the problem of ranking triples for type-like relations like profession and nationality in a knowledge base, achieving top rank in average score difference and second best in Kendall's tau at the WSDM Cup 2017.
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.