Modelling Ontology Evaluation and Validation

Gangemi, Aldo; Catenacci, Carola; Ciaramita, Massimiliano; Lehmann, Jos

doi:10.1007/11762256_13

Cited by 241 publications

(252 citation statements)

References 8 publications

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“… To group and classify pitfalls by categories according to previous ontology quality criteria identified in [3] and [4]. This feature will provide more flexibility to the ontology evaluation, since it will allow users to diagnose their ontologies just with respect to the dimensions they are interested in.…”

Section: Discussionmentioning

confidence: 99%

Did You Validate Your Ontology? OOPS!

Poveda‐Villalón

Suárez-Figueroa

Gómez-Pérez

2015

Lecture Notes in Computer Science

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Abstract. The application of methodologies for building ontologies can improve ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. Several authors have provided lists of typical anomalies detected in ontologies during the last decade. In this context, our aim in this paper is to describe OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.Keywords: pitfalls, bad practices, ontology evaluation, ontology engineering IntroductionThe growing interest during the last decades of practitioners in ontology development methodologies have supposed a step forward in transforming the art of building ontologies into an engineering activity. The correct application of such methodologies benefits ontology quality. However, such quality is not totally guaranteed because developers must tackle a wide range of difficulties and handicaps when modelling ontologies [1,2,5,8]. These difficulties can imply the appearance of the so-called anomalies or bad practices in ontologies. Therefore, it is important to evaluate the ontologies before using or reusing them in other ontologies or semantic applications. One of the crucial issues in ontology evaluation is the identification of anomalies in the ontologies. In this regard, it is worth mentioning that Rector et al. [8] describe a set of common errors made by developers during the ontology modelling. Moreover, Gómez-Pérez [4] proposes a classification of errors identified during the evaluation of different features such as consistency, completeness, and conciseness in ontology taxonomies. Finally, Poveda et al. [7] identify an initial catalogue of common pitfalls.In this context, our goal within this paper is to present an automated tool to help ontology practitioners by detecting common pitfalls during the ontology development. This tool is called OOPS! (OntOlogy Pitfall Scanner!) and represents a new option for ontology developers within ontology evaluation tools as it enlarges the list of errors detected by most recent and available works (e.g. MoKi 1 [6] and XD Analyzer 2 ). In addition, OOPS! can be executed independently of the ontology development plat-1 https://moki.fbk.eu/moki/tryitout/index.php/Main_Page (Last visit on 14-04-2012) 2 http://neon-toolkit.org/wiki/XDTools (Last visit on: 14-04-2012)

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Section: Discussionmentioning

confidence: 99%

Did You Validate Your Ontology? OOPS!

Poveda‐Villalón

Suárez-Figueroa

Gómez-Pérez

2015

Lecture Notes in Computer Science

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“…The work proposed by (Gangemi et al, 2006) defines some measures for assessing an ontology, and evaluates these measures by means of a meta-ontology against which the ontology under validation is compared. This work does not seem to address the semantic correctness of the ontology but mainly its structure and engineering methodology.…”

Section: Related Workmentioning

confidence: 99%

Mutating OWLs: Semantic Mutation Testing for Ontologies

Bartolini

2016

Proceedings of the International Workshop on domAin Specific Model-Based AppRoaches to vErificaTion and validaTiOn

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Abstract:Ontologies are an essential component of semantic knowledge bases and applications, and nowadays they are used in a plethora of domains. Despite the maturity of ontology languages, support tools and engineering techniques, the testing and validation of ontologies is a field which still lacks consolidated approaches and tools. This paper attempts at partly bridging that gap, taking a first step towards the extension of mutation testing techniques to ontologies expressed in a widely-used format. Mutation testing techniques, revisited in the light of the peculiar features of the ontology language and structure, can help in the engineering and refinement of ontologies and software based on them.

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“…Some of these attempts have defined a generic quality evaluation framework [6,9,10,17], other authors proposed to evaluate ontologies depending on the final (re)use of them [18], others have proposed quality models based on features, criteria and metrics [8,3], and in recent times methods for pattern-based evaluation have also emerged [5, 14,]. A summary of generic guidelines and specific techniques for ontology evaluation can be found on [16].…”

Section: State Of the Artmentioning

confidence: 99%

Validating Ontologies with OOPS!

Poveda‐Villalón

Suárez-Figueroa

Gómez-Pérez

2012

Lecture Notes in Computer Science

119

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Abstract. Ontology quality can be affected by the difficulties involved in ontology modelling which may imply the appearance of anomalies in ontologies. This situation leads to the need of validating ontologies, that is, assessing their quality and correctness. Ontology validation is a key activity in different ontology engineering scenarios such as development and selection. This paper contributes to the ontology validation activity by proposing a web-based tool, called OOPS!, independent of any ontology development environment, for detecting anomalies in ontologies. This tool will help developers to improve ontology quality by automatically detecting potential errors. Keywords: ontology, pitfalls, ontology evaluation, ontology validation IntroductionThe emergence of ontology development methodologies during the last decades has facilitated major progress, transforming the art of building ontologies into an engineering activity. The correct application of such methodologies benefits the ontology quality. However, such quality is not always guaranteed because developers must tackle a wide range of difficulties and handicaps when modelling ontologies [1,2,11,15]. These difficulties can imply the appearance of anomalies in ontologies. Therefore, ontology evaluation, which checks the technical quality of an ontology against a frame of reference [18], plays a key role in ontology engineering projects. Ontology evaluation, which can be divided into validation and verification [18], is a complex ontology engineering process mainly due to two reasons. The first one is its applicability in different ontology engineering scenarios, such as development and reuse, and the second one is the abundant number of approaches and metrics [16].One approach for validating ontologies is to analyze whether the ontology is conform to ontology modelling best practices; in other words, to check whether the ontologies contain anomalies or pitfalls. In this regard, a set of common errors made by developers during the ontology modelling is described in [15]. Moreover, in [10] a classification of errors identified during the evaluation of different features such as consistency, completeness, and conciseness in ontology taxonomies is provided. Finally,in [13] authors identify an initial catalogue of common pitfalls.In addition, several tools have been developed to alleviate the dull task of evaluating ontologies. These tools support different approaches like (a) to check the consistency of the ontology, (b) to check the compliance with the ontology language used to

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Modelling Ontology Evaluation and Validation

Cited by 241 publications

References 8 publications

Did You Validate Your Ontology? OOPS!

Did You Validate Your Ontology? OOPS!

Mutating OWLs: Semantic Mutation Testing for Ontologies

Validating Ontologies with OOPS!

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