# Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey

arXiv: https://arxiv.org/abs/2502.19023 · HTML: https://mlsanity.com/paper/2502.19023/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 82 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Ontology Reasoning with Deep Neural Networks](https://mlsanity.com/paper/1808.07980/) (2018)
- [AceKG: A Large-scale Knowledge Graph for Academic Data Mining](https://mlsanity.com/paper/1807.08484/) (2018)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [The DL-Lite Family and Relations](https://mlsanity.com/paper/1401.3487/) (2014)
- [Interactive Debugging of Knowledge Bases](https://mlsanity.com/paper/1605.05950/) (2016)
- [Completing and Debugging Ontologies: state of the art and challenges](https://mlsanity.com/paper/1908.03171/) (2019)
- [Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments](https://mlsanity.com/paper/2202.07980/) (2022)

## Built on by (most cited first, 2 of 2)

- [Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure](https://mlsanity.com/paper/2604.11759/) (2026)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

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.