Detecting and Fixing Inconsistencies in Large Knowledge Graphs*

Abstract

Lately, the availability of massive amounts of data necessitates the adoption of modern representation techniques, such as knowledge graphs (KGs). KGs are typically constructed via automated procedures and by utilizing heterogeneous data sources. This nevertheless hinders the quality of these large resulting KGs, as they might contain contradictions, that is a set of assertions that conflict with some axioms often set by human experts. In turn, classical description logics reasoners cannot be applied as no useful inference results can be generated in the face of inconsistencies. Meanwhile, classical reasoners can be used in order to retrieve the inconsistency explanations, but as the KG size grows larger the required time for this task increases significantly. To address the problem of reasoning with large and inconsistent KGs, we propose an open-source system that detects and fixes inconsistencies by splitting the KG into modules and then processing them in parallel to speed up the process. An empirical evaluation of two datasets illustrates the potential for effective inconsistency detection and fixing of large KGs.