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.

翻译：知识图谱（KGs）作为现实世界事实的结构化表示，是融入人类知识的智能数据库，可辅助机器模拟人类问题解决方式。然而，知识图谱通常规模庞大且不可避免地存在缺失事实，这削弱了基于知识图谱推理的问答系统和推荐系统等应用的效果。知识图谱的链接预测任务旨在通过基于现有知识的推理补全缺失事实。现有研究主要分为两大方向：一是学习实体与关系的低维嵌入以探索潜在模式，二是通过挖掘逻辑规则获得良好可解释性。然而，先前研究未充分考虑现代知识图谱中涉及多种类型实体与关系的异质性。本文提出DegreEmbed模型，该模型结合嵌入学习与逻辑规则挖掘，用于知识图推理。具体而言，我们从节点度视角研究异质知识图谱中的缺失链接预测问题。实验表明，DegreEmbed模型在真实数据集上优于现有最先进方法，且其挖掘的规则具有高质量与可解释性。