解决知识图谱推理中的不一致性问题，三类方法全梳理
Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey
- 从检测、修复到容错推理，系统分析三类应对策略
- 覆盖自动抽取与多源融合导致的不一致场景
- 适合从事知识图谱推理与数据质量研究者参考
知识图谱（KG）通常基于特定本体定义数据模式，推理是实现信息检索、问答和新知识推导等任务的必要手段。然而，用于填充知识图谱的信息常来自自然语言资源的（半）自动抽取，或整合遵循不同语义模式的数据集，导致知识图谱出现不一致性，进而阻碍推理过程。本文综述了在不一致知识图谱上进行推理的方法，重点分析当前技术在三个互补方向上的进展：a）识别引发不一致性的知识图谱部分；b）修复不一致的知识图谱以使其一致；c）支持不一致容忍的推理。文章综合多个相关领域的研究成果，探讨其在上述方向上的关联性与适用条件，并指出现存挑战与未来方向。
原文摘要 · Abstract (English)
In Knowledge Graphs (KGs), where the schema of the data is usually defined by particular ontologies, reasoning is a necessity to perform a range of tasks, such as retrieval of information, question answering, and the derivation of new knowledge. However, information to populate KGs is often extracted (semi-) automatically from natural language resources, or by integrating datasets that follow different semantic schemas, resulting in KG inconsistency. This, however, hinders the process of reasoning. In this survey, we focus on how to perform reasoning on inconsistent KGs, by analyzing the state of the art towards three complementary directions: a) the detection of the parts of the KG that cause the inconsistency, b) the fixing of an inconsistent KG to render it consistent, and c) the inconsistency-tolerant reasoning. We discuss existing work from a range of relevant fields focusing on how, and in which cases they are related to the above directions. We also highlight persisting challenges and future directions.
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