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A knowledge graph completion model based on triple level interaction and contrastive learning
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AI
DOI：10.1016/j.patcog.2024.110783.png)
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Abstract
En 中文
Knowledge graphs provide credible and structured knowledge for downstream tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of knowledge graphs often limits the performance of applications. To address the incompleteness, people have proposed the knowledge graph completion task to supplement the facts of incomplete triplets. Recently, researchers have proposed introducing text descriptions to enrich entity representations. Existing methods based on triple decoupling with text description solve the combinatorial explosion problem well. Nevertheless, they still suffer from a lack of global characteristics of factual triples. In addition, the success of contrastive learning research has improved such methods, but they are still limited by existing negative sampling, which is usually more costly than embedding-based methods. In order to solve these limitations, this paper proposes an innovative triple-level interaction model for knowledge graph completion named InCL-KGC. Concretely, the proposed model employs an on-verge interaction method to reduce text redundancy information for entity representation and capture the global semantics of factual triplets. Furthermore, we design an effective hard negative sampling strategy to improve contrast learning. Additionally, we perform an improved Harbsort algorithm for the purpose of reducing the adverse impact of candidate entity sparsity on inference. Extensive experiment consequences exhibit that our model transcends recent baselines with MRR, Hit@3, and Hits@10 increased by 1.2%, 3.2%, and 6.8% on WN18RR, while the index MRR, Hit@1, Hit@3, and Hits@10 were enhanced by 2.8%, 1%, 3.3%, 4.3% on FB15K-237.
Keywords:
Knowledge graph completion
Link prediction
Semantic matching
Contrastive learning
Negative sampling
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