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Quality Model and Metrics of ontology for Semantic Descriptions of Web Services

Tsinghua Science and Technology 2017年第3期22卷 254-272页

作者： Hong Zhu Dongmei Liu Ian Bayley Arantza Aldea Yunfei Yang Ying Chen Department of Computing and Communication Technologies Oxford Brookes University School of Computer Science and Engineering Nanjing University of Science and Technology

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.

关键词： semantic web services ontology quality model ontology evaluation ontology metrics

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General ontology learning framework

引用

Journal of Southeast University(English Edition) 2006年第3期22卷 381-384页

作者：刘柏嵩高济浙江大学计算机科学与技术学院

In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships between concepts and taxonomy automatic construction are proposed. At the same time ontology evaluation methods are also discussed. The experimental results show that this method produces better performance and it is applicable across different domains. By integrating several machine learning algorithms, this method suffers less ambiguity and can identify domain concepts and relations more accurately. By using generalized corpus WordNet and HowNet, this method is applicable across different domains. In addition, by obtaining source documents from the web on demand, the GOLF can produce up-to-date ontologies.

关键词： ontology ontology learning ontology evaluation semantic web

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