TDN: Triplet Distributor Network for Knowledge Graph Completion

Abstract

Conventional Knowledge Graph Completion (KGC) methods typically map entities and relations to a unified space through the shared mapping matrix, and then interact with entities and relations to infer the missing items in the knowledge graph. Although this shared mapping matrix considers the suitability of all triplets, it neglects the specificity of each triplet. To solve this problem, we dynamically learn one information distributor for each triplet to exchange its specific information. In this paper, we propose a novel Triplet Distributor Network (TDN) for the knowledge graph completion task. Specifically, we adaptively learn one Triplet Distributor (TD) for each triplet to assist the interaction between the entity and relation. Furthermore, on the basis of TD, we creatively design the information exchange layer to dynamically propagate the information of the entity and relation, thus mutually enhancing entity and relation representations. Except for several commonly-used knowledge graph datasets, we still implement the link prediction task on the social-relational and medical datasets to test the proposed method. Experimental results demonstrate that the proposed method performs better than existing state-of-the-art KGC methods. The source codes of this paper are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TDN for Knowledge Graph Completion.git</uri> .