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Semantic clustering analysis for web service discovery and recognition in Internet of Things

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Abstract

Today, semantic web services are rapidly evolving and updating. The discovery of semantic web services is an important concept for the comprehensiveness of individual web services in creating new intelligent systems that meet the complex needs of users and is an important technology in the domain of web services. One of its main goals is to reuse existing web services and combine them in a process that has attracted a lot of attention from different communities like the Internet of Things (IoT). Currently, the discovery of web services in the most common category includes four main methods and a set of sub-methods. Most semantic web service discovery methods are done using semantic descriptions of web services using ontology based on existing pattern recognition approaches. In this research, a new approach is presented that in the first step, the web services description language (WSDL) is scanned and the infrastructure is examined. In the second step, by adding the technique of extracting background information that can be received from the WSDL, the field limitations and existing patterns are considered and detected by semantic spacing on the discovering web services. Also, web services whose parameters contain synonymous synonyms, irregular composite fragments, and similar abbreviations with high accuracy in a cluster contract. The proposed approach is based on a semantic pattern recognition using data mining and finally, the output of the proposed method is single and combined services that have high accuracy and speed of the proposed algorithm in web service discussions.

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Acknowledgements

The research is also funded by Key Scientific Research Projects of Universities in Henan Province (20A880030). The research is funded by Social Science Planning Project of Henan Province in 2020 ‘Research on online education governance mechanism and monitoring system of off-campus training institutions’ (Grant: 2020BYJ030). The research is also funded by Key R&D Plan of Henan Province in 2020 ’Research on online education development strategy of off-campus training institutions: stakeholder perspective’ (Grant: 202400410069).

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Authors and Affiliations

  1. School of Educational Science, Nantong University, Jiangsu, 226019, China

    Xu Fang

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  1. Xu Fang
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XF contributed to conceptualization, methodology, software, writing.

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Correspondence to Xu Fang.

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This material is the authors' own original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere. The paper reflects the authors' own research and analysis in a truthful and complete manner.

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Communicated by Mu-Yen Chen.

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Fang, X. Semantic clustering analysis for web service discovery and recognition in Internet of Things. Soft Comput 27, 1751–1761 (2023). https://doi.org/10.1007/s00500-021-06063-y

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