Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey
Anastasios Nentidis, Charilaos Akasiadis, Angelos Charalambidis, Alexander Artikis
TL;DR
This survey analyzes how to reason over inconsistent Knowledge Graphs by partitioning the problem into inconsistency detection, KG fixing, and inconsistency-tolerant reasoning. It surveys exact and approximate detection techniques, scalable fixing approaches (distinguishing unreliable vs reliable TBoxes) and methods to select among fixes, and then reviews repair-based and many-valued reasoning frameworks that operate under inconsistency when full repair is infeasible. Key contributions include a taxonomy of detection and fixing methods, an overview of repair semantics (AR, CAR, IAR, ICAR) and their computational trade-offs, and a synthesis of open challenges for scalable, web-scale reasoning with KGs. The work emphasizes combining analytical fixes with machine learning and external knowledge to move toward practical, scalable, and robust reasoning in the presence of inconsistencies.
Abstract
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.