Heterogeneous Views and Spatial Structure Enhancement for triple error detection

Knowledge graph error detection is to identify erroneous triples in knowledge graphs that are inconsistent with objective facts in the real world. In practice, the quality of knowledge graphs is an indispensable foundation for the widespread and accurate knowledge application services such as intelligent retrieval and human machine dialogue. Technically, the existing knowledge graph error detection methods face the following two problems: few available negative samples of triples, and an uneven data distribution. That distribution is caused by the large disparity in the number of head and tail entities belonging to the same relationship and the disparity in the number of triples with different relationships. To alleviate these problems, this paper proposes an approach based on the H eterogeneous V iews and S patial S tructure E nhancement (HVSSE) in a contrastive learning framework for triple error detection task. Specifically, the heterogeneous views are constructed to include four kinds of triple views, i.e., positive and negative triple views based on head or tail entity co-occurrence. Moreover, Graph-Spatial-Transformer with an explicit spatial structure encoding is designed to fully capture the contextual information of triple nodes. Thereby, driven by the framework of contrastive learning, our HVSSE model can not only learn more discriminative embedding of triples, but also capture the local structure of triples and global contextual information of knowledge graphs. Experimental results on five public datasets indicate that our proposed approach is superior to the state-of-the-art methods, showing its the effectiveness on triple error detection.