Relation correlations-aware graph convolutional network with text-enhanced for knowledge graph embedding

Hong Yu, Jinxuan Tang, Zhihan Peng, Ye Wang

Abstract: Long-tail distribution is a difficult challenge for knowledge graph embedding. We expect to solve the problem by complementing the information through the neighbor aggregation mechanism of GCN. However, the GCN method and its derivations are unable to learn the representation of edges. To address this problem, we propose RCGCN-TE, Relation Correlations-aware Graph Convolutional Network with Text-Enhanced for knowledge graph embedding, which is the first effort to enable GCN to learn the representation of relations directly. First, the pre-trained language model is used to extract semantic information. Then, the relation correlation graph is constructed by defining the relation relevance function based on the co-occurrence pattern and semantic similarity of relations. Finally, two GCNS are designed to learn entities and relations respectively. Experimental results on tasks such as triple classification and link prediction are better than the baseline. For example, Hits@10, Hits@3, and Hits@1 improved by 8.23\(\%\), 37.49\(\%\), and 46.94\(\%\), respectively, on the entity prediction task.

External IDs:dblp:journals/mlc/YuTPW24