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The objective of the triple scoring task in WSDM Cup 2017 is to computerelevance scores for knowledge-base triples of type-like relations. Forexample, consider Julius Caesar who has had various professions, includingPolitician and Author. For two given triples (Julius Caesar, profession,Politician) and (Julius Caesar, profession, Author), the former triple islikely to have a higher relevance score (also called "triple score") becauseJulius Caesar was well-known as a politician and not as an author. Accurateprediction of such triple scores greatly benefits real-world applications, suchas information retrieval or knowledge base query. In these scenarios, beingable to rank all relations (Profession/Nationality) can help improve the userexperience. We propose a triple scoring model which integrates knowledge fromboth latent features and explicit features via an ensemble approach. The latentfeatures consist of representations for a person learned by using a word2vecmodel and representations for profession/nationality values extracted from apre-trained GloVe embedding model. In addition, we extract explicit featuresfor person entities from the Freebase knowledge base. Experimental results showthat the proposed method performs competitively at WSDM Cup 2017, ranking atthe third place with an accuracy of 79.72% for predicting within two places ofthe ground truth score.

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