Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

Jizhou Huang, Wei Zhang, Shiqi Zhao, Shiqiang Ding, Haifeng Wang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4018-4025. https://doi.org/10.24963/ijcai.2017/561

Providing a plausible explanation for the relationship between two related entities is an important task in some applications of knowledge graphs, such as in search engines. However, most existing methods require a large number of manually labeled training data, which cannot be applied in large-scale knowledge graphs due to the expensive data annotation. In addition, these methods typically rely on costly handcrafted features. In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine. Then, we build a pairwise ranking model which employs a convolutional neural network to automatically learn relevant features. The proposed model can be easily trained with backpropagation to perform the ranking task. The experiments show that our method significantly outperforms several strong baselines.
Keywords:
Natural Language Processing: Natural Language Processing
Natural Language Processing: NLP Applications and Tools