Abstract. Interactive ontology debugging incorporates a user who answers queries about entailments of their intended ontology. In order to minimize the amount of user interaction in a debugging session, a user must choose an appropriate query selection strategy. However, the choice of an unsuitable strategy may result in tremendous overhead in terms of time and cost. We present a learning method for query selection which unites the advantages of existing approaches while overcoming their flaws. Our tests show the utility of our approach when applied to a large set of real-world ontologies, its scalability and adequate reaction time allowing for continuous interactivity. Motivation and Concept:Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. Ontology matching aims at generating a set of semantic links, called alignment, between elements of two standalone ontologies describing related domains. The two ontologies together with the produced alignment, called the aligned ontology, may exhibit a very complex fault structure as a consequence of (1) adding many links between the single ontologies at once and since (2) the actual fault may be located in the produced alignment and/or in one or both of the single ontologies, e.g. if a correct link between two concepts "activates" a source of inconsistency in one of the single ontologies. This makes evident that adequate tool assistance in debugging of such ontologies is indispensable.Ontology debugging deals with the following problem: Given an ontology O which does not meet postulated requirements R, 1 the task is to find a subset of axioms in O, called diagnosis, that needs to be altered or eliminated from the ontology in order to meet the given requirements. Generally, there are many alternative diagnoses for one and the same faulty ontology O. The problem is then to figure out the single diagnosis, called target diagnosis D t , that enables to formulate the target ontology O t featuring the user-intended semantics in terms of entailments and non-entailments. The target ontology can be understood as O minus the axioms of D t plus additional axioms EX Dt which can be added in order to regain desired entailments which might have been eliminated together with axioms in D t .In interactive ontology debugging we assume a user, e.g. the author of the faulty ontology or a domain expert, interacting with an ontology debugging system by answering queries about entailments of the desired target ontology O t . Roughly speaking, each query is a set of axioms and the user is queried whether the conjunction of these axioms is entailed by O t . Every positively (negatively) answered query constitutes a positive/entailed (negative/non-entailed) test case fulfilled by O t . Test cases can
When diagnosing a faulty system one is often confronted with a large number of possible fault hypotheses. Sequential Diagnosis (SD) techniques aim at the localization or identification of the actual fault with minimal cost or effort. SD can be viewed as an Active Learning (AL) task where the learner, trying to find some target hypothesis, formulates sequential queries to some oracle, thereby e.g. requesting additional system measurements. Several query selection measures (QSMs) for determining the best query to ask next have been proposed for AL. To date, few of them have been translated to and employed in SD. In this work, we account for this and analyze various QSMs wrt. to the discrimination power of their selected queries within the diagnostic hypotheses space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in SD to choose a query from a pool of candidates. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we devise an efficient heuristic search for optimal queries. As (preliminary) evaluation results indicate, the latter is especially beneficial in applications where query generation is costly, e.g. involving logical reasoning, and hence a pool of query candidates is not (cheaply) available.
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.
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