2019/03/30 by Patrick Rodler, Rodler, Patrick, Michael Eichholzer +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #FOS: Computer and information sciences #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.1904.00317
openalex publication_date 2019/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When ontologies reach a certain size and complexity, faults such as\ninconsistencies, unsatisfiable classes or wrong entailments are hardly\navoidable. Locating the incorrect axioms that cause these faults is a hard and\ntime-consuming task. Addressing this issue, several techniques for\nsemi-automatic fault localization in ontologies have been proposed. Often,\nthese approaches involve a human expert who provides answers to\nsystem-generated questions about the intended (correct) ontology in order to\nreduce the possible fault locations. To suggest as informative questions as\npossible, existing methods draw on various algorithmic optimizations as well as\nheuristics. However, these computations are often based on certain assumptions\nabout the interacting user.\n In this work, we characterize and discuss different user types and show that\nexisting approaches do not achieve optimal efficiency for all of them. As a\nremedy, we suggest a new type of expert question which aims at fitting the\nanswering behavior of all analyzed experts. Moreover, we present an algorithm\nto optimize this new query type which is fully compatible with the (tried and\ntested) heuristics used in the field. Experiments on faulty real-world\nontologies show the potential of the new querying method for minimizing the\nexpert consultation time, independent of the expert type. Besides, the gained\ninsights can inform the design of interactive debugging tools towards better\nmeeting their users' needs.\n