Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017

12/22/2017

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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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Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017

Ensemble of Neural Classifiers for Scoring Knowledge Base Triples

Paraphrasing vs Coreferring: Two Sides of the Same Coin

Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017

Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017

RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017

Budget-Constrained Reinforcement of Ranked Objects

Overview of the Triple Scoring Task at the WSDM Cup 2017

Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017

Related Research

Ensemble of Neural Classifiers for Scoring Knowledge Base Triples

Paraphrasing vs Coreferring: Two Sides of the Same Coin

Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017

Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017

RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017

Budget-Constrained Reinforcement of Ranked Objects

Overview of the Triple Scoring Task at the WSDM Cup 2017