Evaluation of Ontologies and DL Reasoners

Fahad, Muhammad; Qadir, Muhammad Abdul; Shah, Syed Adnan Hussain

doi:10.1007/978-0-387-87685-6_5

Cited by 12 publications

(15 citation statements)

References 13 publications

(23 reference statements)

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“…The authors in [6], criticized the DL reasoner performance by evaluating their consistency in a case study of automobile ontology. They proved that the reasoners such as Racer, Pellet and FaCT++ could not detect various types of errors, such as circulatory errors, semantic inconsistency errors and some types of redundancy errors like disjoint relation and identical formal definition.…”

Section: Related Workmentioning

confidence: 99%

“…Besides the errors, we propose the design anomaly category. In fact, anomalies removal is necessary to improve ontology usability [6]. We present in what follows, the anti-pattern definitions using the DL expressions.…”

Section: Anti-pattern Definitionmentioning

confidence: 99%

“…The correction of these anomalies guarantees the usability of an ontology [6]. Actually, these anomalies include:…”

Section: Generic Design Anomaliesmentioning

confidence: 99%

“…can not deal with the inconsistency, the incompleteness and the redundancy errors. Evaluating such errors is a very difficult task [6].…”

Section: Introductionmentioning

confidence: 99%

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Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Rekik

Boukadi

Gaaloul³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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The lack of standardized descriptions of cloud services hinders their discovery. In an effort to standardize cloud service descriptions, several works propose to use ontologies. Nevertheless, the adoption of any of the proposed ontologies calls for an evaluation to show its efficiency in cloud service discovery. Indeed, the existing cloud providers describe, their similar offered services in different ways. Thus, various existing works aim at standardizing the representation of cloud computing services by proposing ontologies. Since the existing proposals were not evaluated, they might be less adopted and considered. Indeed, the ontology evaluation has a direct impact on its understandability and reusability. In this paper, we propose an evaluation approach to validate our proposed Cloud Service Ontology (CSO), to guarantee an adequate cloud service discovery. To this end, this paper has a three-fold contribution. First, we specify a set of patterns and anti-patterns in order to evaluate our CSO. Second, we define an antipattern detection algorithm based on SPARQL queries which provides a set of correction recommendations to help ontologists revise their ontology. Finally, tests were conducted in relation to: (i) the algorithm efficiency and (ii) anti-pattern detection of design anomalies as well as taxonomic and domain errors within CSO.

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Section: Related Workmentioning

confidence: 99%

Section: Anti-pattern Definitionmentioning

confidence: 99%

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Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Rekik

Boukadi

Gaaloul³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…can not deal with the inconsistency, the incompleteness and the redundancy errors. Evaluating such errors is a very difficult task [12].…”

Section: Introductionmentioning

confidence: 99%

AntiPattren-based cloud ontology evaluation

Loukil

Rekik

Boukadi

2016

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)

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Abstract-Nowadays, cloud computing is an emerging technology thanks to its ability to provide on-demand computing services (hardware and software) with less description standardization effort. Multiple issues and challenges in discovering cloud services appear due to the lack of the cloud service description standardization. In fact, the existing cloud providers describe, their similar offered services in different ways. Thus, various existing works aim at standardizing the representation of cloud computing services while proposing ontologies. However, since the existing proposals were not evaluated, they might be less adopted and considered. Indeed, the ontology evaluation has a direct impact on its understandability and reusability. In this paper, we propose an evaluation approach to validate our proposed Cloud Service Ontology (CSO), to guarantee an adequate cloud service discovery. This paper contribution is threefold. First, it specifies a set of patterns and anti-patterns in order to evaluate CSO. Second, it defines an anti-pattern detection method based on SPARQL queries which provides a set of correction recommendations to help ontologists revise the ontology. Finally, some experiment tests were conducted in relation to: (i) the method efficiency and (ii) anti-pattern detection of design anomalies as well as taxonomic and domain errors within CSO.

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