Pattern-based OWL Ontology Debugging Guidelines

Debugging inconsistent OWL ontologies is a tedious and time-consuming task where a combination of ontology engineers and domain experts is often required to understand whether the changes to be performed are actually dealing with formalisation errors or changing the intended meaning of the original knowledge model. Debugging services from existing ontology engineering tools and debugging strategies available in the literature aid in this task. However, in complex cases they are still far from providing adequate support to ontology developers, due to their lack of efficiency or precision when explaining the main causes for unsatisfiable classes, together with little support for proposing solutions for them. We claim that it is possible to provide additional support to ontology developers, based on the identification of common antipatterns and a debugging strategy, which can be combined with the use of existing tools in order to make this task more effective

Catalogue of Anti-Patterns for formal Ontology debugging

Debugging of inconsistent OWL ontologies is normally a tedious and time-consuming task where a combination of ontology engineers and domain expert is often required to understand whether the changes to be performed in order to make the OWL ontology consistent are actually changing the intended meaning of the original knowledge model. This task is aided by existing ontology debugging systems, incorporated in existing reasoners and ontology engineering tools, which ameliorate this problem but in complex cases are still far from providing adequate support to ontology engineers, due to lack of efficiency or lack of precision in determining the main causes for inconsistencies. In this paper we describe a set of anti-patterns commonly found in OWL ontologies, which can be useful in the task of ontology debugging in combination with those debugging tools

Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)

With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e. the missing relations in the is-a hierarchy. Previous work has dealt with the case of taxonomies. In this work we extend the scope to deal with ALC ontologies that can be represented using acyclic terminologies. We present algorithms and discuss a system

OnlynessIsLoneliness (OIL)

Our work is based on the debugging process of real ontologies that have been developed by domain experts, who are not necessarily too familiar with DL, and hence can misuse DL constructors and misunderstand the semantics of some OWL expressions, leading to unwanted unsatisfiable classes. Our patterns were first found during the debugging process of a medium-sized OWL ontology (165 classes) developed by a domain expert in the area of hydrology [9]. The first version of this ontology had a total of 114 unsatisfiable classes. The information provided by the debugging systems used ([3], [5]) on (root) unsatisfiable classes was not easily understandable by domain experts to find the reasons for their unsatisfiability. And in several occasions during the debugging process the generation of justifications for unsatisfiability took several hours, what made these tools hard to use, confirming the results described in [8]. Using this debugging process and several other real ontologies debugging one, we found out that in several occasions domain experts were just changing axioms from the original ontology in a somehow random manner, even changing the intended meaning of the definitions instead of correcting errors in their formalisatio

SPARQL-DL queries for antipattern detection

Ontology antipatterns are structures that reflect ontology modelling problems, they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. Antipatterns normally appear in ontologies developed by those who are not experts in ontology engineering. Based on our experience in ontology design, we have created a catalogue of such antipatterns in the past, and in this paper we describe how we can use SPARQL-DL to detect them. We conduct some experiments to detect them in a large OWL ontology corpus obtained from the Watson ontology search portal. Our results show that each antipattern needs a specialised detection method

Antipattern detection in web ontologies: an experiment using SPARQL queries

Ontology antipatterns are structures that reflect ontology modelling problems because they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. We propose four methods for the detection of antipatterns using SPARQL queries.We conduct some experiments to detect antipattern in a corpus of OWL ontologies

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses