Complexity metrics for ontology based information

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

doi:10.1504/ijtm.2009.024120

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Most of the frameworks developed for these purposes focus on analysing the asserted class hierarchy using basic metrics, such as the numbers of root classes, numbers of leaf classes and depth of the inheritance tree, to determine structural metrics of the ontology [25,27,1,19]. A more in-depth approach to analysing the asserted class graph is proposed in [12], which applies methods from Social Network Analysis for the purpose of comparing ontologies.…”

Section: Related Workmentioning

confidence: 99%

The Justificatory Structure of the NCBO BioPortal Ontologies

Bail

Horridge

Parsia

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. Current ontology development tools offer debugging support by presenting justifications for entailments of OWL ontologies. While these minimal subsets have been shown to support debugging and understanding tasks, the occurrence of multiple justifications presents a significant cognitive challenge to users. In many cases even a single entailment may have many distinct justifications, and justifications for distinct entailments may be critically related. However, it is currently unknown how prevalent significant numbers of multiple justifications per entailment are in the field. To address this lack, we examine the justifications from an independently motivated corpus of actively used biomedical ontologies from the NCBO BioPortal. We find that the majority of ontologies contain multiple justifications, while also exhibiting structural features (such as patterns) which can be exploited in order to reduce user effort in the ontology engineering process.

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Section: Related Workmentioning

confidence: 99%

The Justificatory Structure of the NCBO BioPortal Ontologies

Bail

Horridge

Parsia

et al. 2011

The Semantic Web – ISWC 2011

View full text Add to dashboard Cite

show abstract

“…Most of existing ontology measurement frameworks 12,14,15,16 mainly consider ontology structure and seldom measure ontological semantics. Ensan and Du proposed four metrics NSLD, NMLD, NSED and NMED, which concentrate on measuring the semantics of ontologies for modular ontologies 24 .…”

Section: Related Workmentioning

confidence: 99%

“…By the extensive analysis of most of the existing ontology metrics, we summarize the measurement entities of the existing ontology metrics 12,13,14,15,16,17,24,25 , most of which mainly include concepts/classes, properties, class inheritance, axioms, instances, fanouts, fanins, paths and partitions. In these types of measurement entities, classes, properties, class inheritance, axioms and instances belong to the fine-grained types of measurement entities because they are the basic elements consisting of ontologies.…”

Section: Entity Summarizationmentioning

confidence: 99%

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A Generic Implementation Framework for Measuring Ontology-Based Information

Ma¹,

Zhang²,

Jin³

et al. 2014

IJCIS

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As a knowledge representation tool, ontologies have been widely applied in many fields such as knowledge management and information integration, etc. Ontology measurement is an important challenge in the field of knowledge management in order to manage the development of ontology based systems and reduce the risk of project failure. This paper proposes a generic implementation framework for stable semantic ontology measurement. Through this framework, an ontology will be measured according to its semantic enriched representation model (SERM). The SERM model of an ontology can be used for stably measuring the semantics of the ontology. Then ontology metrics are integrated into the framework to measure candidate ontologies according to its SERM model. The related experiments are made to show that the framework can effectively measure the semantics of ontologies.

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Stable cohesion metrics for evolving ontologies

et al. 2011

J. Softw. Maint. Evol.: Res. Pract.

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With the drastic development of semantic‐driven applications, assessing the quality of ontologies has received more attention. Measuring and assessing the quality of ontologies can help ontology engineers to control project management and reduce the risk of project failures. However, most of the existing ontology metrics for measuring and assessing the quality of ontologies are defined based on ontology structure, and neglect the stability of ontology measurement. In this paper, we concentrate on stable ontology measurement by using semantically derived ontology metrics. We propose four ontology cohesion metrics, which fully consider the implicitly expressed semantic information and are defined based on ontological semantics rather than ontology structure. Before measuring and assessing an ontology, we materialize a pre‐processing for stable ontology measurement by treating the ontology. The proposed ontology cohesion metrics are theoretically validated by the validation criteria of object‐oriented software. The experimental results show that we can successfully collect more semantic knowledge from the testing ontologies for stable ontology measurement by using the proposed ontology cohesion metrics. The ontology cohesion metrics proposed in this paper can be reasonably used as a cogent complementarity of existing ontology metrics. Copyright © 2010 John Wiley & Sons, Ltd.

show abstract

Complexity metrics for ontology based information

Cited by 7 publications

References 13 publications

The Justificatory Structure of the NCBO BioPortal Ontologies

The Justificatory Structure of the NCBO BioPortal Ontologies

A Generic Implementation Framework for Measuring Ontology-Based Information

Stable cohesion metrics for evolving ontologies

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