Improving Effectiveness of Knowledge Graph Generation Rule Creation and Execution

Chapter 3: Rule-driven inconsistency resolution for rule refinement

Inconsistencies are introduced in graphs when ontology terms are used without adhering to restrictions given by the ontologies, and this affects the graphs' quality. Possible root causes for these inconsistencies include:

In previous research efforts, a method to resolve inconsistencies by automatically refining the corresponding rules has been developed. However, this method assumes that used ontologies align with the user's envisioned semantic model, which is not always the case. More, when a high number of rules are involved in inconsistencies users have no insights regarding the order in which rules should be inspected.

Whereas Chapter 2 contributes to rule creation, this chapter contributes to rule refinement, by describing Resglass: our rule-driven method for the resolution of inconsistencies in ontologies and rules. We address Research Question 3 "How can we score and rank rules and ontology terms for inspection to improve the manual resolution of inconsistencies?" and validate Hypothesis 3 "The automatic inconsistency-driven ranking of Resglass improves, compared to a random ranking, by at least 20% the overlap with experts' manual ranking."

As this is a cumulative dissertation, we refer to the publication "Rule-driven inconsistency resolution for knowledge graph generation rules" for the remainder of the chapter.

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