Path-based reasoning with K-nearest neighbor and position embedding for knowledge graph completion

Knowledge graph completion aims to perform link prediction between entities. Reasoning over paths in incomplete knowledge graphs has become a hot research topic. However, most of the existing path reasoning methods ignore both the overlapping phenomenon of paths between similar relations and the order information of relations in paths, and they only consider the obvious paths between entities. To address the problems of knowledge graph reasoning, a new path-based reasoning method with K -Nearest Neighbor and position embedding is proposed in this paper. The method first projects entities and relations to continuous vector space, and then utilizes the idea of the K -Nearest Neighbor algorithm to find the K nearest neighbors of each relation. After that, the paths of similar relations are merged. Then, paths are modeled through the combination operations on relations. The position information of the relations is considered during the combination, that is, the position embedding is added to the relation vector in the path. A series of experiments are conducted on real datasets to prove the effectiveness of the proposed method. The experimental results show that the proposed method significantly outperforms all baselines on entity prediction and relation prediction tasks.