Measuring the understandability of deduction rules for OWL

Debugging OWL ontologies can be aided with automated reasoners that generate entailments, including undesirable ones. This information is, however, only useful if developers understand why the entailments hold. To support domain experts (with limited knowledge of OWL), we are developing a system that explains, in English, why an entailment follows from an ontology. In planning such explanations, our system starts from a justification of the entailment and constructs a proof tree including intermediate statements that link the justification to the entailment. Proof trees are constructed from a set of intuitively plausible deduction rules. We here report on a study in which we collected empirical frequency data on the understandability of the deduction rules, resulting in a facility index for each rule. This measure forms the basis for making a principled choice among alternative explanations, and identifying steps in the explanation that are likely to require extra elucidation

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

RaDON - Repair and Diagnosis in Ontology Networks

One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users

Ontology based e-Learning approach over Traditional e-Learning

This research paper covers the possible enhancement on tradition e-Learning approach to Ontology based e-Learning approach. Traditional e-Learning environment has the number of limitation. These limitation of the tradition e-Learning will overcome in proposed ontology based e-Learning approach. The first section of this paper covers the growth of information technology in education sector for e-Learning. The second section describes about the e-learning. The third section of this paper shows personalized e-learning. The forth section highlights the problem faced by the e-learner. The fifth section shows the proposed approach of modern ontology based e-learning technique which will overcome the limitations of the current e-Learning methods. Six section is about the various tools used to create ontology for proposed e-learning approach in this research paper. The benefits of proposed approach are listed in the seventh section

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