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Ensemble of Neural Classifiers for Scoring Knowledge Base Triples

Abstract · Mar 15, 2017 04:00 ·

cs-cl cs-ir

Arxiv Abstract

  • Ikuya Yamada
  • Motoki Sato
  • Hiroyuki Shindo

This paper describes our approach for the triple scoring task at WSDM Cup 2017. The task aims to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks. We propose an approach wherein the outputs of multiple neural network classifiers are combined using a supervised machine learning model. The experimental results show that our proposed method achieves the best performance in one out of three measures, and performs competitively in the other two measures.

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