Detecting and Fixing Inconsistency of Large Knowledge Graphs
Authors: 
Charilaos Akasiadis, Anastasios Nentidis, Angelos Charalambidis, Alexander ArtikisAuthors Info & Claims
Charilaos Akasiadis, Anastasios Nentidis, Angelos Charalambidis, Alexander ArtikisAuthors Info & ClaimsArticle No.: 14, Pages 1 - 8
Abstract
Modern Knowledge Graphs (KG) are typically constructed via automated workflows that mine information from vast amounts of, often heterogeneous, resources. Such KG are typically large and also contain formal inconsistencies, i.e. assertions that induce conflicts when combined with given axioms. Such formal inconsistencies hinder the application of classical Description Logics reasoners, as no meaningful results can be obtained by reasoning. Classical reasoners can be used with such KG to retrieve explanations for the inconsistencies, however as the KG size grows larger, the time required for this operation increases dramatically. In this paper we put forward an open-source system for detecting and fixing formal inconsistencies in large real-world knowledge graphs. We integrate and extend the state-of-the-art for parallel KG inconsistency detection and fixing in a single framework. The empirical evaluation of our method on variants of the LUBM dataset reveals its potential for effective inconsistency detection and fixing in large KG.
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- Detecting and Fixing Inconsistency of Large Knowledge Graphs
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September 2024
437 pages
ISBN:9798400709821
DOI:10.1145/3688671
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Published: 27 December 2024
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SETN 2024
SETN 2024: 13th Hellenic Conference on Artificial Intelligence
September 11 - 13, 2024
Piraeus, Greece
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