JKDM: A Joint Structural and Semantic Diffusion-Generated Knowledge Completion Model

Abstract

Knowledge Graph Completion (KGC) aims to predict missing triples in a graph based on known relationships between entities. However, most KGC methods face the challenge of diversification representations among entities, making it difficult for models to link entities effectively. This article proposes a Joint Knowledge (Structure-Semantics) Diffusion Model (JKDM) to capture entity diversification relationships. By leveraging the probabilistic generative capabilities of diffusion models, JKDM generates diversification outputs that align with the distribution of target entities rather than producing a single deterministic result. Considering the insufficient structural information of sparse entities, which leads to their representations tending toward a smooth distribution, making it difficult for diffusion models to learn their probability distributions, we jointly enhance sparse entity representations using structural and semantic information. Structurally, a Dual-channel Graph Attention Network (DGAT) is introduced to capture structural embeddings of entities from different perspectives. Semantically, a contextual path strategy is applied to pre-trained language models (PLMs) to enrich entity semantics. Under the condition of joint embeddings, JKDM gradually denoises to generate the probability distribution of target entities. Experiments demonstrate that JKDM outperforms SOTA methods on the FB15k-237, WN18RR, and UMLS datasets, achieving improvements of 2.3%, 1.5%, and 0.43% in MRR scores, respectively.