Abstract

Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human problem solving. However, KGs are usually huge and there are inevitably missing facts in KGs, thus undermining applications such as question answering and recommender systems that are based on knowledge graph reasoning. Link prediction for knowledge graphs is the task aiming to complete missing facts by reasoning based on the existing knowledge. Two main streams of research are widely studied: one learns low-dimensional embeddings for entities and relations that can explore latent patterns, and the other gains good interpretability by mining logical rules. Unfortunately, the heterogeneity of modern KGs that involve entities and relations of various types is not well considered in the previous studies. In this paper, we propose DegreEmbed, a model that combines embedding-based learning and logic rule mining for inferring on KGs. Specifically, we study the problem of predicting missing links in heterogeneous KGs from the perspective of the degree of nodes. Experimentally, we demonstrate that our DegreEmbed model outperforms the state-of-the-art methods on real world datasets and the rules mined by our model are of high quality and interpretability.

中文翻译：

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DegreEmbed：将实体嵌入纳入逻辑规则学习中以进行知识图推理

 抽象的

知识图谱（KG）作为现实世界事实的结构化表示，是包含人类知识的智能数据库，可以帮助机器模仿人类解决问题的方式。然而，知识图谱通常非常庞大，并且知识图谱中不可避免地会丢失事实，从而损害了基于知识图推理的问答和推荐系统等应用。知识图谱的链接预测是旨在基于现有知识进行推理来补全缺失事实的任务。两个主要的研究流得到了广泛的研究：一是学习可以探索潜在模式的实体和关系的低维嵌入，二是通过挖掘逻辑规则获得良好的可解释性。不幸的是，先前的研究并没有很好地考虑涉及各种类型的实体和关系的现代知识图谱的异质性。在本文中，我们提出了 DegreEmbed，一种结合基于嵌入的学习和逻辑规则挖掘来推断知识图谱的模型。具体来说，我们从节点度的角度研究了异构知识图谱中缺失链接的预测问题。通过实验，我们证明我们的 DegreEmbed 模型优于现实世界数据集上最先进的方法，并且我们的模型挖掘的规则具有高质量和可解释性。