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<p>Relation prediction in knowledge graphs is a classic task. However, predicting unseen facts under the open-world assumption remains challenging, as conventional closure reasoning methods tend to fail. To address this, we propose an uncertain knowledge graph embedding and Bayesian inferring model, UKGEBN, which examines the capabilities of approximate inference under the open-world assumption. This model employs a large language model to encode entities and relations by treating facts as natural language sentences. It then learns knowledge confidence through a recurrent neural network and subsequently constructs Bayesian networks for the uncertain knowledge graph based on the learned confidence. The constructed Bayesian network can reasonably infer unseen knowledge facts, even if the elements of these facts-such as new entities and relationships-have never been encountered in the knowledge base before. The experimental results show that the new model has improved the MSE metric by 24.6% in relation prediction on the benchmark dataset. Case studies indicate that the model significantly enhances the revelation of latent semantic information, facilitating the prediction of implicit relationships in uncertain knowledge graphs.</p>

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Prediction of Implicit Relationships Using Uncertain Knowledge Graph Embedding and Bayesian Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Shihan Yang,
  • Liannan Lin,
  • Chunjiang Liu,
  • Mingkai Zhang,
  • Haiyi Yang

摘要

Relation prediction in knowledge graphs is a classic task. However, predicting unseen facts under the open-world assumption remains challenging, as conventional closure reasoning methods tend to fail. To address this, we propose an uncertain knowledge graph embedding and Bayesian inferring model, UKGEBN, which examines the capabilities of approximate inference under the open-world assumption. This model employs a large language model to encode entities and relations by treating facts as natural language sentences. It then learns knowledge confidence through a recurrent neural network and subsequently constructs Bayesian networks for the uncertain knowledge graph based on the learned confidence. The constructed Bayesian network can reasonably infer unseen knowledge facts, even if the elements of these facts-such as new entities and relationships-have never been encountered in the knowledge base before. The experimental results show that the new model has improved the MSE metric by 24.6% in relation prediction on the benchmark dataset. Case studies indicate that the model significantly enhances the revelation of latent semantic information, facilitating the prediction of implicit relationships in uncertain knowledge graphs.