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<p>Knowledge Graph Completion(KGC) tasks play a crucial role in applications such as intelligent question answering and recommender systems by inferring missing entity relationships from known knowledge graph information. However, existing research faces three key limitations: (1) insufficient ability to capture and represent textual information, (2) inability to effectively handle complex relationships, such as many-to-one and one-to-many, and (3) failure to effectively learn the structural information of the knowledge graph. To address the above challenges, this paper proposes Text Embedding Optimized Knowledge Graph Completion (TEOKGC). First, the model employs a dual text encoder, utilizing the pre-trained Sentence-BERT language model to enhance the semantic representation of text features. Second, to improve the model’s ability to handle complex relationships, such as many-to-one and one-to-many, this paper introduces a deformable adaptive attention module to uncover latent text features more effectively. Finally, to enhance the learning of knowledge graph topology, this paper introduces a path learning optimization mechanism that improves text embedding by computing inter-entity path distances based on triad similarity fusion. The proposed model, TEOKGC, is evaluated on multiple public datasets, WN18RR, FB15k-237, and Wikidata5M. Experimental results demonstrate that TEOKGC achieves significant improvements, verifying the effectiveness and potential of our model. Our code is available at <a href="https://github.com/herenohere/TEOKGC">https://github.com/herenohere/TEOKGC</a>.</p>

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TEOKGC: text embedding optimization for knowledge graph completion

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Hai Huan,
  • Tong Zhu

摘要

Knowledge Graph Completion(KGC) tasks play a crucial role in applications such as intelligent question answering and recommender systems by inferring missing entity relationships from known knowledge graph information. However, existing research faces three key limitations: (1) insufficient ability to capture and represent textual information, (2) inability to effectively handle complex relationships, such as many-to-one and one-to-many, and (3) failure to effectively learn the structural information of the knowledge graph. To address the above challenges, this paper proposes Text Embedding Optimized Knowledge Graph Completion (TEOKGC). First, the model employs a dual text encoder, utilizing the pre-trained Sentence-BERT language model to enhance the semantic representation of text features. Second, to improve the model’s ability to handle complex relationships, such as many-to-one and one-to-many, this paper introduces a deformable adaptive attention module to uncover latent text features more effectively. Finally, to enhance the learning of knowledge graph topology, this paper introduces a path learning optimization mechanism that improves text embedding by computing inter-entity path distances based on triad similarity fusion. The proposed model, TEOKGC, is evaluated on multiple public datasets, WN18RR, FB15k-237, and Wikidata5M. Experimental results demonstrate that TEOKGC achieves significant improvements, verifying the effectiveness and potential of our model. Our code is available at https://github.com/herenohere/TEOKGC.