A Survey on Ontology Metrics

García, Juan Ramón Carbó; García-Peñalvo, Francisco José; Therón, Roberto

doi:10.1007/978-3-642-16318-0_4

Cited by 35 publications

(20 citation statements)

References 11 publications

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“…Most existing ontology metrics are structural, see Ref. [25] for a survey. Tao et al [26] used the number of root classes, the number of leaf classes, and the average depth of inheritance tree as cohesion metrics to measure modular relatedness of OWL ontologies.…”

Section: Related Workmentioning

confidence: 99%

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Quality model and metrics of ontology for semantic descriptions of web services

Zhu

Liu

Bayley

et al. 2017

Tinshhua Sci. Technol.

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An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.

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Section: Related Workmentioning

confidence: 99%

“…As far as we know, there is no other account of using a gold standard in the definition of ontology metrics [10,25] . (2) Language Independence: The metrics are defined based on an abstract general model of ontologies rather than on the concrete syntax of any specific ontology definition language.…”

Section: Main Contribution Of the Workmentioning

confidence: 99%

Quality model and metrics of ontology for semantic descriptions of web services

Zhu

Liu

Bayley

et al. 2017

Tinshhua Sci. Technol.

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“…A variety of ontology metrics is available for assessing ontologies (Garcia et al 2010). In this paper, we use the statistical ontology metrics from the Protégé software and the BioPortal web service.…”

Section: Ontology Evaluationmentioning

confidence: 99%

Ontology of core data mining entities

Panov

Soldatova

Džeroski

2014

Data Min Knowl Disc

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In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines the most essential data mining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend. OntoDM-core is available at

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“…Most metrics of ontologies evaluate the structure of the ontology instead of its semantics (García et al, 2010;Ma et al, 2010). A framework for a broader evaluation of different types of ontologies is hard to find.…”

Section: Ontology Evaluationmentioning

confidence: 99%

Engineering ontologies for question answering

et al. 2014

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Using an ontology to automatically generate questions for ordinary people requires a structure and concepts compliant with human thought. Here we present methods to develop a pragmatic, expert-based and a basic-level ontology and a framework to evaluate these ontologies. Comparing these ontologies shows that expert-based ontologies are most easy to construct but lack required cognitive semantic characteristics. Basic-level ontologies have structure and concepts which are better in terms of cognitive semantics but are most expensive to construct.

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A Survey on Ontology Metrics

Cited by 35 publications

References 11 publications

Quality model and metrics of ontology for semantic descriptions of web services

Quality model and metrics of ontology for semantic descriptions of web services

Ontology of core data mining entities

Engineering ontologies for question answering

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