基于规则传递性和反对称性的知识图谱嵌入
Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of Rules
摘要：
知识图谱的表示学习将实体和关系类型编码到连续的低维向量空间中，学习实体和关系类型的嵌入。现有的方法大多只关注知识三元组，而忽略了包含丰富背景知识的逻辑规则。虽然已经有一些工作旨在利用知识三元组和逻辑规则，他们忽略了逻辑规则的传递性和反对称性。本文提出了一种新的方法来学习知识表示与实体和有序关系的知识和逻辑规则。其核心思想是将知识三元组与逻辑规则相结合，利用逻辑规则的传递性和反对称性，对逻辑规则中的关系类型进行近似排序。关系类型的嵌入的所有条目都被约束为非负。我们将一般的约束优化问题转化为无约束优化问题来求解非负矩阵分解。实验结果表明，我们的模型显着优于其他基线上的知识图谱完成任务。这表明我们的模型能够捕捉到传递性和反对称性信息，这在知识图谱的嵌入学习中具有重要意义。
英文摘要：
Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Most existing methods only concentrate on knowledge triples, ignoring logic rules which contain rich background knowledge. Although there has been some work aiming at leveraging both knowledge triples and logic rules, they ignore the transitivity and antisymmetry of logic rules. In this paper, we propose a novel approach to learn knowledge representations with entities and ordered relations in knowledges and logic rules. The key idea is to integrate knowledge triples and logic rules, and approximately order the relation types in logic rules to utilize the transitivity and antisymmetry of logic rules. All entries of the embeddings of relation types are constrained to be non-negative. We translate the general constrained optimization problem into an unconstrained optimization problem to solve the non-negative matrix factorization. Experimental results show that our model significantly outperforms other baselines on knowledge graph completion task. It indicates that our model is capable of capturing the transitivity and antisymmetry information, which is significant when learning embeddings of knowledge graphs.
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。
