Are query-based ontology debuggers really helping knowledge engineers?

Abstract

Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontology-based systems, were proposed throughout recent years. Query-based debugging is a comparably recent interactive approach that localizes the true cause of an observed problem by asking knowledge engineers a series of questions. Concrete implementations of this approach exist, such as the OntoDebug plug-in for the ontology editor Protégé.To validate that a newly proposed method is favorable over an existing one, researchers often rely on simulation-based comparisons. Such an evaluation approach however has certain limitations and often cannot fully inform us about a method's true usefulness. We therefore conducted different user studies to assess the practical value of query-based ontology debugging. One main insight from the studies is that the considered interactive approach is indeed more efficient than an alternative algorithmic debugging based on test cases. We also observed that users frequently made errors in the process, which highlights the importance of a careful design of the queries that users need to answer.

Debugging Classical Ontologies Using Defeasible Reasoning Tools

“…In the same way that Rodler et al [20,21,11] could motivate the necessity of an interactive ontology debugging methodology by arguing that without it, valuable axioms are often deleted thus leading to a loss of knowledge, our extension can also be motivated: without a strategy showing how axioms could be weakened rather than deleted, valuable knowledge may be lost.…”

“…Thus far, we have worked with model-based diagnosis for debugging ontologies. The heuristic approach to debugging tries to find common patterns of faulty ontology modelling and presents suggestions for repairs based on this [21]. The benefit of using the heuristic approach is that, especially with large ontologies, computation of repairs is more efficient as minimal conflict sets do not need to be computed for each inconsistency before returning a result.…”

Ontologization of tools for modeling and synthesis technological process trajectories

“…• consultations with various specialists to eliminate contradictions and inaccuracies. Regarding tools for working with ontologies, the leading position is held by the Stanford development Protégé, a freeware editor of open source ontologies, which at the same time serves as a structure for creating intelligent systems [14,15]. Moreover, as noted on the official website of Protégé, "it is supported by a strong community of academic, government and corporate users who use it to create knowledge-based solutions" (https://protege.stanford.edu) in various subject areas.…”

Anticipatory Troubleshooting

“…Most works use techniques such as greedy heuristics and information gain; e.g., measuring the amount of information that observation Y tells us about the diagnosis state X. Some researchers present locally optimal solutions that run in linear time [28][29][30]. Rodler [29] proposes to use active learning techniques in order to achieve a nearly optimal sequence of queries.…”

“…Some researchers present locally optimal solutions that run in linear time [28][29][30]. Rodler [29] proposes to use active learning techniques in order to achieve a nearly optimal sequence of queries. It should be noted that there are techniques that concentrate on saving system tests (e.g., a test that shows whether the system returned to its healthy state) and therefore consider batch repair in a period of time [6,31] rather than fix one component in each iteration [3].…”