Path-Aware Few-Shot Knowledge Graph Completion

Abstract

Few-shot knowledge graph completion (FKGC) has emerged as a significant area of interest for addressing the long-tail problem in knowledge graphs. Traditional approaches often focus on the sparse few-shot neighborhood to derive semantic representation, overlooking other critical information forms such as relation paths. In this article, we introduce an innovative method, called PARE, which fully leverages relation paths to enhance the few-shot representation by simultaneously incorporating both neighborhood and relation path information. Inspired by the principles of information transmission, PARE directly models relation paths between entities and parameterizes the information interference within different relation paths. Through parameter learning, PARE effectively captures information propagation along relation paths while mitigating the influence of relation dependency. To preserve neighborhood information, we employ a two-step neighborhood aggregator to resolve few-shot neighbors’ ambiguity and develop a reconstruction module. By integrating the representations of relation paths and contextual neighborhoods, we achieve a comprehensive few-shot representation for two given entities. We utilize a matching processor for knowledge triplet evaluation. Extensive experiments demonstrate that our PARE model outperforms state-of-the-art baselines on widely-used benchmark datasets.