A multi-view representation learning framework for commonsense knowledge bases

Abstract

Commonsense knowledge bases play an essential role in a wide range of natural language processing tasks. This paper studies the problem of representation learning for commonsense knowledge bases to effectively incorporate their knowledge into numerical models. Most existing knowledge base representation learning methods are difficult to apply to commonsense knowledge bases since they are much sparser than general knowledge bases. Hence, in this paper, we propose a novel method for commonsense knowledge base representation learning. Specifically, we first model the nodes from multiple views, including word/phase information, context information, and graph information. Then, we design a scoring function to measure whether the commonsense triplets are established through relation representation learning. We conduct extensive experiments on two tasks and the results show that our proposed model outperforms other knowledge base representation learning methods.