The Evaluation of Ontologies

Obrst, Leo; Ceusters, Werner; Mani, Inderjeet; Ray, Steven R.; Smith, Barry

doi:10.1007/978-0-387-48438-9_8

Cited by 112 publications

(67 citation statements)

References 21 publications

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“…Vrandečić (2009) discusses some quality criteria for ontology verification. Obrst et al (2007) suggests requirements for ontology validation. Neuhaus et al (2014) reports existing best practices and tools in the field of ontology evaluation.…”

Section: Detecting Hidden Errorsmentioning

confidence: 99%

Detecting hidden errors in an ontology using contextual knowledge

Teymourlouie¹,

Zaeri²,

Nematbakhsh³

et al. 2018

Expert Systems with Applications

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Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent.

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Section: Detecting Hidden Errorsmentioning

confidence: 99%

Detecting hidden errors in an ontology using contextual knowledge

Teymourlouie¹,

Zaeri²,

Nematbakhsh³

et al. 2018

Expert Systems with Applications

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“…A fragment of the NIST SP800-41 best practice catalogue is presented in Table 1. Section 7 provides a scenario driven approach [43] whereby the best practice catalogues encoded as Semantic Threat Graphs can be reasoned over.…”

Section: Evaluation Of Semantic Threat Graphs For Best Practicementioning

confidence: 99%

Management of security policy configuration using a Semantic Threat Graph approach

Foley

Fitzgerald

2011

JCS

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Managing the configuration of heterogeneous enterprise security mechanisms is a complex task. The effectiveness of a configuration may be constrained by poor understanding and/or management of the overall security policy requirements, which may, in turn, unnecessarily expose the enterprise to known threats. This paper proposes a threat management based approach, whereby knowledge about the effectiveness of mitigating countermeasures is used to guide the autonomic configuration of security mechanisms. This knowledge is modeled in terms of Semantic Threat Graphs, a variation of the traditional Threat/Attack Tree, extended in order to relate semantic information about security configuration with threats, vulnerabilities and countermeasures. An ontology-based approach to representing and reasoning over this knowledge is taken. A case study based on Network Access Controls demonstrates how threats can be analysed and how automated configuration recommendations can be made based on catalogues of countermeasures. These countermeasures are drawn from best-practice standards, including NIST, IETF and PCI-DSS recommendations for firewall configuration.

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“…Ontologies are considered significant and reusable as they contained core knowledge structures that require rigor for both development and evaluation. To keep rigor, multiple parameters are checked and detailed criteria is considered for evaluation of ontologies by various researchers [3], [9]. The criteria listed by Vrandevic [16] contains accuracy, adaptability, clarity, completeness, computational efficiency, conciseness, consistency, and other parameters for evaluation.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Approach for Evaluating Sufficiency of Ontologies

Sanagavarapu

Gollapudi

Chimalakonda

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract1 Ontologies have emerged as a common way of representing knowledge. Recently, people with minimal domain background or ontology engineering are developing ontologies, leading to a corpus of informal and under-evaluated ontologies. Existing ontology evaluation approaches require rigorous application of formal methods and knowledge of domain experts that can be cumbersome or tedious. We propose a lightweight approach for evaluating sufficiency of ontologies based on Natural Language Processing techniques. The approach consists of verifying the extent of coverage of concepts and relationships of ontologies against words in domain corpus. As a case study, we applied our approach to evaluate sufficiency of ontology in two example domains -Education (Curriculum) and Security (Phishing). We show that our approach yields promising results, is less effort intensive and is comparable with existing evaluation methods.

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The Evaluation of Ontologies

Cited by 112 publications

References 21 publications

Detecting hidden errors in an ontology using contextual knowledge

Detecting hidden errors in an ontology using contextual knowledge

Management of security policy configuration using a Semantic Threat Graph approach

A Lightweight Approach for Evaluating Sufficiency of Ontologies

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