Measuring effectiveness of ontology debugging systems

2014 ◽  
Vol 71 ◽  
pp. 169-186 ◽  
Author(s):  
Qiu Ji ◽  
Zhiqiang Gao ◽  
Zhisheng Huang ◽  
Man Zhu
Keyword(s):  
Ontology Debugging

A Graph-Based Approach to Ontology Debugging in DL-Lite

2015 ◽  
pp. 33-46
Author(s):  
Xuefeng Fu ◽  
Yong Zhang ◽  
Guilin Qi
Keyword(s):  
Ontology Debugging

Base Revision for Ontology Debugging

2008 ◽  
Vol 19 (5) ◽  
pp. 721-743 ◽  
Author(s):  
M. M. Ribeiro ◽  
R. Wassermann
Keyword(s):  
Base Revision ◽  
Ontology Debugging

scholarly journals Interactive ontology debugging: Two query strategies for efficient fault localization

2012 ◽  
Vol 12-13 ◽  
pp. 88-103 ◽  
Author(s):  
Kostyantyn Shchekotykhin ◽  
Gerhard Friedrich ◽  
Philipp Fleiss ◽  
Patrick Rodler
Keyword(s):  
Fault Localization ◽  
Ontology Debugging

scholarly journals Reasoning with Concept Diagrams about Antipatterns

10.29007/4ckv ◽  
2018 ◽  
Author(s):  
Zohreh Shams ◽  
Mateja Jamnik ◽  
Gem Stapleton ◽  
Yuri Sato
Keyword(s):  
Empirical Evidence ◽  
Empirical Studies ◽  
Visual Representation ◽  
Inference Rules ◽  
Reasoning Process ◽  
Representation Language ◽  
Owl Ontology ◽  
One To One ◽  
Ontology Debugging ◽  
Do So

Ontologies are notoriously hard to define, express and reason about. Many tools have been developed to ease the debugging and the reasoning process with ontologies, however they often lack accessibility and formalisation. A visual representation language, concept diagrams, was developed for expressing and reasoning about ontologies in an accessible way. Indeed, empirical studies show that concept diagrams are cognitively more accessible to users in ontology debugging tasks. In this paper we answer the question of “ How can concept diagrams be used to reason about inconsistencies and incoherence of ontologies?”. We do so by formalising a set of inference rules for concept diagrams that enables stepwise verification of the inconsistency and/or incoherence of a set of ontology axioms. The design of inference rules is driven by empirical evidence that concise (merged) diagrams are easier to comprehend for users than a set of lower level diagrams that offer a one-to-one translation of OWL ontology axioms into concept diagrams. We prove that our inference rules are sound, and exemplify how they can be used to reason about inconsistencies and incoherence. Finally, we indicate how our rules can serve as a foundation for new rules required when representing ontologies in diverse new domains.

scholarly journals Generalized Abstract Argumentation: A First-order Machinery towards Ontology Debugging

2010 ◽  
Vol 14 (46) ◽  
Author(s):  
Martin Oscar Moguillansky ◽  
Nicolás Daniel Rotstein ◽  
Marcelo Alejandro Falappa
Keyword(s):  
First Order ◽  
Abstract Argumentation ◽  
Ontology Debugging

Interactive Ontology Debugging: Two Query Strategies for Efficient Fault Localization

2012 ◽  
Author(s):  
Kostyantyn Shchekotykhin ◽  
Gerhard Friedrich ◽  
Philipp Fleiss ◽  
Patrick Rodler
Keyword(s):  
Fault Localization ◽  
Ontology Debugging

scholarly journals Debugging Classical Ontologies Using Defeasible Reasoning Tools

2021 ◽  
Author(s):  
Simone Coetzer ◽  
Katarina Britz
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Description Logics ◽  
Defeasible Reasoning ◽  
Successful Implementation ◽  
Knowledge Engineer ◽  
Logical Consistency ◽  
Definition Of ◽  
Ontology Debugging ◽  
Relevant Domain

A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.

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