EM-RBR：一个基于推理的知识图谱完善框架
EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
摘要：
知识图谱补全的目的是预测知识图谱中给定实体的新链接。目前主流的嵌入方法主要关注知识库中包含的事实三元组，而忽略了知识库中隐含的逻辑规则所提供的丰富背景信息。为了解决这个问题，在本文中，我们提出了一个通用的框架，名为EM-RBR（嵌入和基于规则的推理），能够结合推理的优势，基于规则和国家的最先进的嵌入模型。EM-RBR旨在利用规则中包含的关系背景知识进行多关系推理链接预测，而不是嵌入模型中的表面向量三角链接。通过这种方式，我们可以在更深的背景下探索两个实体之间的关系，以达到更高的准确性。在实验中，我们证明了EM-RBR在FB 15 k，WN 18和我们的新数据集FB 15 k-R上与以前的模型相比具有更好的性能，特别是新数据集，我们的模型比那些最先进的模型表现得更好。我们在https://github.com/1173710224/link-prediction-with-rule-based-reasoning上提供EM-RBR的实现。
英文摘要：
Knowledge graph completion aims to predict the new links in given entities among the knowledge graph (KG). Most mainstream embedding methods focus on fact triplets contained in the given KG, however, ignoring the rich background information provided by logic rules driven from knowledge base implicitly. To solve this problem, in this paper, we propose a general framework, named EM-RBR(embedding and rule-based reasoning), capable of combining the advantages of reasoning based on rules and the state-of-the-art models of embedding. EM-RBR aims to utilize relational background knowledge contained in rules to conduct multi-relation reasoning link prediction rather than superficial vector triangle linkage in embedding models. By this way, we can explore relation between two entities in deeper context to achieve higher accuracy. In experiments, we demonstrate that EM-RBR achieves better performance compared with previous models on FB15k, WN18 and our new dataset FB15k-R, especially the new dataset where our model perform futher better than those state-of-the-arts. We make the implementation of EM-RBR available at https://github.com/1173710224/link-prediction-with-rule-based-reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
