Discovery of Semantic Web Services Compositions Based on SAWSDL Annotations

Hobold, Guilherme C.; Siqueira, Frank

doi:10.1109/icws.2012.97

Cited by 25 publications

(11 citation statements)

References 7 publications

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“…OWLS-MX [7] described services in semantic OWL-S, and exploited logic-based reasoning for parameters matching and service retrieval. In [8], semantic annotations SAWSDL were used to automatically find service compositions. Xiao et.al.…”

Section: A Service Discoverymentioning

confidence: 99%

A Probabilistic Approach for Web Service Discovery

Zhang

Huai

et al. 2013

2013 IEEE International Conference on Services Computing

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Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDLbased keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS information is not always available. In this paper, we propose a probabilistic service discovery approach to help web service users to retrieve related services and to improve the search performance. Specifically, we apply a probabilistic model to characterize the latten topics between services and queries, and then propose a matching method based on the topic relevance. Experiments on services from a real service repository confirm the feasibility and efficiency of this proposed method.

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Section: A Service Discoverymentioning

confidence: 99%

A Probabilistic Approach for Web Service Discovery

Zhang

Huai

et al. 2013

2013 IEEE International Conference on Services Computing

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“…Therefore, we can conclude that only a small portion of historical mahups can be perceived similar with the test instance and be included in collaborative filtering for service recommendation. The remaining mashups' similarity values are too low to have significant contributions to the recommendation performance according to equation (8). These dissimilar mashups can be neglected to save computation cost without harming precision.…”

Section: Impact Of Kmentioning

confidence: 99%

“…Zhao et al [32] constructed a semantic Bayesian network based on the semi-supervised learning method for the recommendation. A recent work [8] focused on services described in semantic languages to automate the process of service discovery. A hybrid approach was proposed in [12], which combined semantic-based content matching and QoS prediction.…”

Section: Semantic-aware Recommendationmentioning

confidence: 99%

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Time-Aware Service Recommendation for Mashup Creation

Zhong

Fan

Huang

et al. 2015

IEEE Trans. Serv. Comput.

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Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, service ecosystem is evolving over time with services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of service ecosystem on service recommendation. This paper employs a probabilistic approach to predict the popularity of services to enhance the recommendation performance. A method is presented that extracts service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware service recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-service usage in an evolving service ecosystem. Experiments on a real-world service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information.

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“…This problem is addressed by SAWSDL (Semantic Annotations for WSDL) [6], which allows the extension of WSDL descriptions with semantic annotations from arbitrary ontologies. As one example for a more heavy-weight semantic description language, we also cover OWL-S (Web Ontology Language for Web Services) [17] in our evaluation.…”

Section: Service Descriptionmentioning

confidence: 99%