What Are Ontologies Good For?

Horrocks, Ian

doi:10.1007/978-3-642-34997-3_9

Cited by 26 publications

(13 citation statements)

References 22 publications

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“…The Celestina Data data model is implemented by means of an ontology. Generally speaking, this is an information model to capture a shared understanding of a domain of interest for a particular application [47,48,49]. The ontology of Celestina Data is specified using the OWL Web Ontology Language, an open standard of the World Wide Web Consortium that is used to encode ontologies in a machine-readable format [50].…”

Section: The Celestina Data Data Modelmentioning

confidence: 99%

Next directions in experimental data for seismic hazard mitigation

Martínez

Williams

Dyke³

et al. 2017

Engineering Structures

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Data are one of the main assets of earthquake engineering. Laboratory experiments can be extremely expensive and time consuming to replicate and, therefore, long-term preservation of experimental data and sharing the data with users has become one of the disciplinary priorities. There is a growing demand for international partnerships, which creates a need for data sharing, in an attempt to maximise research impact and to tackle experimental setups that could not be realised otherwise. However, there is a patent lack of interoperability between the institutions forming the earthquake engineering community, which inhibits efficient collaboration between them. In this paper, we discuss a vision about the directions that experimental data should take in the coming years, focusing on two aspects: enhanced international collaborations and implementation of open data access. We also describe the progress that has been made towards this vision, by establishing an open platform for the integration of earthquake hazard mitigation resources called Celestina. Celestina is supported by Semantic Web technologies, and uses an ontology as its integration data model. A prototype of the platform has been developed and tested between NEES (Purdue University, in the US), the University of Oxford (in UK) and EUCENTRE (in Italy), and a small proof of concept has enabled integrated experimental data from Oxford and EUCENTRE through the NEES cyberenvironment. This demonstration provides an example which has the potential to catalyze a new generation of research progress enabled by international data sharing.

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Section: The Celestina Data Data Modelmentioning

confidence: 99%

Next directions in experimental data for seismic hazard mitigation

Martínez

Williams

Dyke³

et al. 2017

Engineering Structures

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“…In this sense, they act much like a vocabulary, similarly to a database. They have found their place as the basis of knowledge representation in many application fields, from web searches to the medical and legal domains [22].…”

Section: Introductionmentioning

confidence: 99%

Software Testing Techniques Revisited for OWL Ontologies

Bartolini

2017

Communications in Computer and Information Science

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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 some traditional software testing techniques to ontologies expressed in a widely-used format. Mutation testing and coverage testing, revisited in the light of the peculiar features of the ontology language and structure, can can assist in designing better test suites to validate them, and overall help in the engineering and refinement of ontologies and software based on them.

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Section: Introductionmentioning

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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What Are Ontologies Good For?

Cited by 26 publications

References 22 publications

Next directions in experimental data for seismic hazard mitigation

Next directions in experimental data for seismic hazard mitigation

Software Testing Techniques Revisited for OWL Ontologies

Mutating OWLs: Semantic Mutation Testing for Ontologies

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