Analysing Large Inconsistent Knowledge Graphs Using Anti-patterns

Thomas de Groot*, Joe Raad, Stefan Schlobach

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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A number of Knowledge Graphs (KGs) on the Web of Data contain contradicting statements, and therefore are logically inconsistent. This makes reasoning limited and the knowledge formally useless. Understanding how these contradictions are formed, how often they occur, and how they vary between different KGs is essential for fixing such contradictions, or developing better tools that handle inconsistent KGs. Methods exist to explain a single contradiction, by finding the minimal set of axioms sufficient to produce it, a process known as justification retrieval. In large KGs, these justifications can be frequent and might redundantly refer to the same type of modelling mistake. Furthermore, these justifications are –by definition– domain dependent, and hence difficult to interpret or compare. This paper uses the notion of anti-pattern for generalising these justifications, and presents an approach for detecting almost all anti-patterns from any inconsistent KG. Experiments on KGs of over 28 billion triples show the scalability of this approach, and the benefits of anti-patterns for analysing and comparing logical errors between different KGs.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publication18th International Conference, ESWC 2021, Virtual Event, June 6–10, 2021, Proceedings
EditorsRuben Verborgh, Katja Hose, Heiko Paulheim, Pierre-Antoine Champin, Maria Maleshkova, Oscar Corcho, Petar Ristoski, Mehwish Alam
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Electronic)9783030773854
ISBN (Print)9783030773847
Publication statusPublished - 2021
Event18th European Semantic Web Conference, ESWC 2021 - Virtual, Online
Duration: 6 Jun 202110 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12731 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th European Semantic Web Conference, ESWC 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Inconsistency
  • Linked open data
  • Reasoning


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