Many AI applications rely on knowledge about a relevant real-world domain that is encoded by means of some logical knowledge base (KB). The most essential benefit of logical KBs is the opportunity to perform automatic reasoning to derive implicit knowledge or to answer complex queries about the modeled domain. The feasibility of meaningful reasoning requires KBs to meet some minimal quality criteria such as logical consistency. Without adequate tool assistance, the task of resolving violated quality criteria in KBs can be extremely tough even for domain experts, especially when the problematic KB includes a large number of logical formulas or comprises complicated logical formalisms. 
Published non-interactive debugging systems often cannot localize all possible faults (incompleteness), suggest the deletion or modification of unnecessarily large parts of the KB (non-minimality), return incorrect solutions which lead to a repaired KB not satisfying the imposed quality requirements (unsoundness) or suffer from poor scalability due to the inherent complexity of the KB debugging problem. Even if a system is complete and sound and considers only minimal solutions, there are generally exponentially many solution candidates to select one from. However, any two repaired KBs obtained from these candidates differ in their semantics in terms of entailments and non-entailments. Selection of just any of these repaired KBs might result in unexpected entailments, the loss of desired entailments or unwanted changes to the KB. 
This work proposes complete, sound and optimal methods for the interactive debugging of KBs that suggest the one (minimally invasive) error correction of the faulty KB that yields a repaired KB with exactly the intended semantics. Users, e.g. domain experts, are involved in the debugging process by answering automatically generated queries about the intended domain.

Interactive Debugging of Knowledge Bases.

Applications of semantic systems require their users to design ontologies that correctly formalize knowledge about a domain. In many cases factors such as insufficient understanding of a knowledge representation language, problems concerning modeling techniques and granularity, or inability to foresee all implications of formulated axioms result in faulty ontologies.

OntoDebug: Interactive Ontology Debugging Plug-in for Protégé

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

/pdf/anytime-diagnosis-for-reconfiguration-16y7b39lqw.pdf

Anytime diagnosis for reconfiguration

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.

/pdf/on-active-learning-strategies-for-sequential-diagnosis-35oo0u0zab.pdf

On Active Learning Strategies for Sequential Diagnosis.

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing why a system under examination is not behaving as expected. Given an abstract description (model) of the system's components and their behavior when functioning normally, MBD techniques rely on observations about the actual system behavior to reason about possible causes when there are discrepancies between the expected and observed behavior. Due to its generality, MBD has been successfully applied in a variety of application domains over the last decades.

In many application domains of MBD, testing different hypotheses about the reasons for a failure can be computationally costly, e.g., because complex simulations of the system behavior have to be performed. In this work, we therefore propose different schemes of parallelizing the diagnostic reasoning process in order to better exploit the capabilities of modern multi-core computers. We propose and systematically evaluate parallelization schemes for Reiter's hitting set algorithm for finding all or a few leading minimal diagnoses using two different con flict detection techniques. Furthermore, we perform initial experiments for a basic depth-first search strategy to assess the potential of parallelization when searching for one single diagnosis. Finally, we test the effects of parallelizing "direct encodings" of the diagnosis problem in a constraint solver.

/pdf/parallel-model-based-diagnosis-on-multi-core-computers-526igtrsim.pdf

Parallel model-based diagnosis on multi-core computers

Sequential diagnosis methods compute a series of queries for discriminating between diagnoses. Queries are answered by probing such that eventually the set of faults is identified. The computation of queries is based on the generation of a set of most probable diagnoses. However, in diagnosis problem instances where the number of minimal diagnoses and their cardinality is high, even the generation of a set of minimum cardinality diagnoses is unfeasible with the standard conflict-based approach. In this paper we propose to base sequential diagnosis on the computation of some set of minimal diagnoses using the direct diagnosis method, which requires less consistency checks to find a minimal diagnosis than the standard approach. We study the application of this direct method to high cardinality faults in knowledge-bases. In particular, our evaluation shows that the direct method results in almost the same number of queries for cases when the standard approach is applicable. However, for the cases when the standard approach is not applicable, sequential diagnosis based on the direct method is able to locate the faults correctly.

Sequential diagnosis of high cardinality faults in knowledge-bases by direct diagnosis generation

Debugging is an important prerequisite for the wide-spread application of ontologies, especially in areas that rely upon everyday users to create and maintain knowledge bases, such as the Semantic Web. Most recent approaches use diagnosis methods to identify sources of inconsistency. However, in most debugging cases these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit probabilities of typical user errors to formulate information theoretic concepts for query selection. Our evaluation showed that the suggested method reduces the number of required observations compared to myopic strategies.

pdf/query-strategy-for-sequential-ontology-debugging-2vmboe2c41.pdf

Query strategy for sequential ontology debugging

Modern ontology debugging methods allow efficient identification and localization of faulty axioms defined by a user while developing an ontology. However, in many use cases such as ontology alignment the ontologies might include many conflict sets, i.e. sets of axioms preserving the faults, thus making the ontology diagnosis infeasible. In this paper we present a debugging approach based on a direct computation of diagnoses that omits calculation of conflict sets. The proposed algorithm is able to identify diagnoses for an ontology which includes a large number of faults and for which application of standard diagnosis methods fails. The evaluation results show that the approach is practicable and is able to identify a fault in adequate time.

/pdf/on-direct-debugging-of-aligned-ontologies-dq1kdpesen.pdf

On direct debugging of aligned ontologies

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Sequential diagnosis of high cardinality faults in knowledge-bases by direct diagnosis generation

Query strategy for sequential ontology debugging

On direct debugging of aligned ontologies