文献类型：期刊文献

英文题名：Commonsense Knowledge Base Completion with Relational Graph Attention Network and Pre-trained Language Model

作者：Ju, Jinhao[1]; Yang, Deqing[1]; Liu, Jingping[2]

机构：[1] School of Data Science, Fudan University, Shanghai, China; [2] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2022

起止页码：4104

外文期刊名：International Conference on Information and Knowledge Management, Proceedings

收录：EI(收录号：20224413038022)

语种：英文

外文关键词：Computational linguistics - Natural language processing systems

摘要：Many commonsense knowledge graphs (CKGs) still suffer from incompleteness although they have been applied in many natural language processing tasks successfully. Due to the scale and sparsity of CKGs, existing knowledge base completion models are not still competent for CKGs. In this paper, we propose a commonsense knowledge base completion (CKBC) model which learns the structural representations and contextual representations of CKG nodes and relations, respectively by a relational graph attention network and a pre-trained language model. Based on these two types of representations, the scoring decoder in our model achieves a more accurate prediction for a given triple. Our empirical studies on the representative CKG ConceptNet demonstrate our model's superiority over the state-of-the-art CKBC models. ? 2022 ACM.