Knowledge Graph Error Detection with Hierarchical Path Structure

Knowledge graphs (KGs) play a pivotal role in AI-related applications. In order to construct or continuously enrich KGs, automatic knowledge construction and update mechanisms are usually utilized, which inevitably bring in plenty of noise, and noise would degrade the performance of downstream applications. Existing KG error detection methods utilize the embeddings of entities and relations, or directly leverage the paths between entities to test the plausibility of triples, while ignore the valuable hierarchical information contained in the paths between entities. Indeed, the paths between a pair of entities conform to a hierarchical structure. Specifically, there may be a number of paths between two entities, and each path is comprised of several relations. The hierarchical structure is able to provide precious information, and is beneficial to leverage the path information in a fine-grained manner. To this end, in this paper, we propose a novel model named KG error detection with HiErarchical pAth stRucture (HEAR for short). Particularly, for a given triple, HEAR first learns path representations with the relations contained in the path, then integrates all path representations, and at last predicts the plausibility of the triple. Finally, we extensively validate the superiority of HEAR against various state-of-the-art baselines.