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Rule-driven inconsistency resolution for knowledge graph generation rules

In Semantic Web (2019)

Knowledge graphs, which contain annotated descriptions of entities and their interrelations, are often generated using rules that apply semantic annotations to certain data sources. (Re)using ontology terms without adhering to the axioms defined by their ontologies results in inconsistencies in these graphs, affecting their quality. Methods and tools were proposed to detect and resolve inconsistencies, the root causes of which include rules and ontologies. However, these either require access to the complete knowledge graph, which is not always available in a time-constrained situation, or assume that only generation rules can be refined but not ontologies. In the past, we proposed a rule-driven method for detecting and resolving inconsistencies without complete knowledge graph access, but it requires a predefined set of refinements to the rules and does not guide users with respect to the order the rules should be inspected. We extend our previous work with a rule-driven method, called Resglass, that considers refinements for generation rules as well as ontologies. In this article, we describe Resglass, which includes a ranking to determine the order with which rules and ontology elements should be inspected, and its implementation. The ranking is evaluated by comparing the manual ranking of experts to our automatic ranking. The evaluation shows that our automatic ranking achieves an overlap of 80% with experts ranking, reducing this way the effort required during the resolution of inconsistencies in both rules and ontologies.