標題: Automated knowledge discovery and semantic annotation for network and web services
作者: Lin, Szu-Yin
Chung, Chia-Chen
Hu, Wei-Che
Hung, Chihli
Chen, Shih-Lun
Lin, Ting-Lan
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Web services;semantic annotation;knowledge discovery in services;OWL-S
公開日期: 1-Jul-2016
摘要: With the rise of the Internet of things, the smart environmental issue is becoming increasingly important. Sensor web is one of the best solutions to this issue and provides the advantages of sensor networks and web services. Ontology web language for services (OWL-S) is an OWL-based web services ontology, which provides the ability to describe the semantics of web services and their capabilities in a formal and machine-processable manner. Moreover, it aids semantic service matching, selection and composition. However, automatically annotating semantic web services is a highly complicated and tedious task. In this study, we propose a methodology to uncover information in the history data and profiles of web services and then semantically annotate them. With the proposed approach, semantic relationships between web services could be extracted via a combination of association rules and input/ output matching. Our results show that this hybrid automated knowledge-discovery approach works better than traditional approaches do. We also provide a scenario to explain how the proposed methodology works.
URI: http://dx.doi.org/10.1177/1550147716657925
http://hdl.handle.net/11536/132570
ISSN: 1550-1477
DOI: 10.1177/1550147716657925
期刊: INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Volume: 12
Issue: 7
起始頁: 0
結束頁: 0
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