Cohesion Metrics for Ontology Design and Application

Yao, Haining; Orme, Anthony M.; Etzkorn, Letha H.

doi:10.3844/jcssp.2005.107.113

Cited by 122 publications

(74 citation statements)

References 10 publications

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“…Semantic Web documents that define or contribute to the definition of classes and properties. Most [13,14] employ content analysis on ontologies with various foci, such as building a comprehensive evaluation framework [15], qualifying concept consistency [16,17], quantifying the graph structure of class and property taxonomy hierarchy [18][19][20][21], and measuring the structure and the instance space of a given ontology [22]. These studies have been limited in two ways.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Semantic Web on the Web

Ding

Finin

2006

Lecture Notes in Computer Science

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Abstract. Semantic Web languages are being used to represent, encode and exchange semantic data in many contexts beyond the Web -in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the Web aspect of the Semantic Web -its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution.

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Section: Introductionmentioning

confidence: 99%

Characterizing the Semantic Web on the Web

Ding

Finin

2006

Lecture Notes in Computer Science

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“…In [16] three metrics, namely, number of root classes, number of leaf classes, and average depth of inheritance tree to measure the cohesiveness of an ontology are presented. This study was inspired from the principle of cohesiveness in an Object-Oriented class diagram presented by [15].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Advanced Complexity Metrics of Biomedical Ontologies in the Bioportal Repository

Kazadi¹,

Fonou-Dombeu²

2017

IJBBB

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Abstract:There is an increase in the number of biomedical ontologies on the semantic web. Therefore, it is important to evaluate their complexity to promote their sharing and reuse in the biomedical domain. This study analyses and discusses the advanced complexity features of the biomedical ontologies stored in the BioPortal repository. A set of 100 biomedical ontologies from the BioPortal repository was collected. Thereafter, the collected ontologies are assigned to the analysis process to compute their advanced complexity metrics including the: size of the vocabulary, entropy of ontology graphs, the average number of paths per class, the tree impurity, class richness, percentage of part-of relations in the total number of relations, and many more. The results show that the biomedical ontologies studied are highly complex; this finding is evidenced by the analysis of their size of the vocabulary, average number of paths and entropy of ontology graph. However, it was interesting to learn that the structure of these ontologies favour their easy reuse and maintenance; these findings were reached through the analysis of the tree impurity, class and relationship richness of these ontologies.

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“…of Root Classes metric (norc) [17] corresponds to the number of root classes (without superclasses) explicitly defined. Let C the classes in an ontology:…”

Section: Datasetmentioning

confidence: 99%

“…of Leaf Classes metric (nolc) [17] is the sum of all leaf classes, i.e., those without subclasses, in an ontology. Let C the set of classes in an ontology:…”

Section: Datasetmentioning

confidence: 99%

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Empirical findings on ontology metrics

Sicilia¹,

Rodríguez²,

García‐Barriocanal³

et al. 2012

Expert Systems with Applications

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Ontologies are becoming the preferred way of representing, dealing and reasoning with large volumes of information in several domains. In consequence, the creation, evaluation and maintenance of ontologies has become an engineering process that needs to be managed and measured using sound and reliable methods. As part of any ontology engineering or revision process, metrics can play a role helping in identifying possible problems or incorrect use of ontology elements, along with providing a kind of quality assessment that complements reviews requiring expert inspection. However, in spite of the fact that there are a number of ontology metric proposals described in the literature, there is a lack of empirical studies that provide a basis for their interpretation. This paper reports a systematic exploration of existing ontology metrics from a large set of ontologies extracted from the Swoogle search engine. The Ontorank value used inside the Swoogle to rank search results is used as a contrast for some existing metrics. The results show that the existing proposed metrics evaluated in general do not seem to be indicators of that ranking, but they can be helpful to identify particular kinds of ontologies.

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Cohesion Metrics for Ontology Design and Application

Cited by 122 publications

References 10 publications

Characterizing the Semantic Web on the Web

Characterizing the Semantic Web on the Web

Analysis of Advanced Complexity Metrics of Biomedical Ontologies in the Bioportal Repository

Empirical findings on ontology metrics

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