Abstract
When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user. In this work, we characterize and discuss different user types and show that existing approaches do not achieve optimal efficiency for all of them. As a remedy, we suggest a new type of expert question which aims at fitting the answering behavior of all analyzed experts. Moreover, we present an algorithm to optimize this new query type which is fully compatible with the (tried and tested) heuristics used in the field. Experiments on faulty real-world ontologies show the potential of the new querying method for minimizing the expert consultation time, independent of the expert type. Besides, the gained insights can inform the design of interactive debugging tools towards better meeting their users' needs.
Abstract (translated)
当本体达到一定的规模和复杂性时，诸如不一致、不可满足的类或错误的蕴涵等错误是很难避免的。找出导致这些故障的不正确的公理是一项艰巨而耗时的任务。针对这一问题，提出了本体半自动故障定位的几种方法。通常，这些方法涉及到一个人类专家，他为系统生成的关于预期（正确）本体的问题提供答案，以减少可能的故障位置。为了尽可能地提出信息性问题，现有的方法利用各种算法优化和启发式方法。然而，这些计算通常基于有关交互用户的某些假设。
URL
https://arxiv.org/abs/1904.00317
