Explainable Link Prediction in Knowledge Hypergraphs

Link prediction in knowledge hypergraphs has been recognized as a critical issue in various downstream tasks for knowledge-enabled applications, from question answering to recommender systems. However, most existing approaches are primarily performed in a black-box fashion, which learn low-dimensional embeddings for inference, thus cannot provide human-understandable interpretation. In this paper, we present HyperMLN, an n-ary, mixed, and explainable framework that interprets the path-reasoning process with first-order logic, which provides a knowledge-enhanced interpretable prediction framework, in which domain knowledge in the logic rules improves the performance of embedding models, while semantic information in the embedding space can optimize the weight of the logic rules in turn. To provide benchmark rule sets for explainable link prediction methods, three types of meta-logic rules in each popular dataset are mined for interpreting results. While achieving explainability, our framework also realizes an average improvement of 3.2% on Hits@1 compared to the state-of-the-art knowledge hypergraph embedding method. Our code is available at https://github.com/zirui-chen/HyperMLN.