1 research outputs found
Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017
The objective of the triple scoring task in WSDM Cup 2017 is to compute
relevance scores for knowledge-base triples of type-like relations. For
example, consider Julius Caesar who has had various professions, including
Politician and Author. For two given triples (Julius Caesar, profession,
Politician) and (Julius Caesar, profession, Author), the former triple is
likely to have a higher relevance score (also called "triple score") because
Julius Caesar was well-known as a politician and not as an author. Accurate
prediction of such triple scores greatly benefits real-world applications, such
as information retrieval or knowledge base query. In these scenarios, being
able to rank all relations (Profession/Nationality) can help improve the user
experience. We propose a triple scoring model which integrates knowledge from
both latent features and explicit features via an ensemble approach. The latent
features consist of representations for a person learned by using a word2vec
model and representations for profession/nationality values extracted from a
pre-trained GloVe embedding model. In addition, we extract explicit features
for person entities from the Freebase knowledge base. Experimental results show
that the proposed method performs competitively at WSDM Cup 2017, ranking at
the third place with an accuracy of 79.72% for predicting within two places of
the ground truth score.Comment: Triple Scorer at WSDM Cup 2017, see arXiv:1712.0808