论文标题
知识图上的神经，符号和神经符号推理
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs
论文作者
论文摘要
知识图推理是支持机器学习应用程序（例如信息提取，信息检索和建议）的基本组成部分。由于知识图可以视为知识的离散符号表示，因此在知识图上的推理可以自然利用符号技术。但是，符号推理对模棱两可和嘈杂的数据不宽容。相反，深度学习的最新进展促进了知识图上的神经推理，这对模棱两可和嘈杂的数据是可靠的，但是与象征性推理相比缺乏可解释性。考虑到两种方法的优势和缺点，最近在结合两种推理方法方面做出了努力。在这项调查中，我们详细介绍了知识图上的符号，神经和混合推理的发展。我们调查了两项特定的推理任务，知识图的完成以及在知识图上回答问题，并在统一的推理框架中解释它们。我们还简要讨论了知识图推理的未来方向。
Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs 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, neural and hybrid 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.
