Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.
翻译：知识图表推理是支持诸如信息提取、信息检索和建议等机器学习应用的基本组成部分。由于知识图表可以被视为知识的离散象征性表示,知识图表推理自然可以发挥象征性技术的作用。然而,象征性推理却不容忍模糊和吵闹的数据。相反,最近深层次的学习进展促进了知识图表的神经推理,而知识图表对模糊和吵闹的数据是强有力的,但与象征性推理相比,缺乏可解释性。考虑到这两种方法的利弊,最近努力将两种推理方法结合起来。在本次调查中,我们透彻地研究了知识图表上的象征性推理、神经力推理和神经-同步推理的发展。我们调查了两个具体的推理任务,即知识图表的完成和对知识图表的回答,并在一个统一的推理框架中加以解释。我们还简要讨论了知识图表推理的未来方向。
相关内容
微信扫码咨询专知VIP会员