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Mining and clustering service goals for RESTful service discovery

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Abstract

In recent years, RESTful services that are mainly described using short texts are becoming increasingly popular. The keyword-based discovery technology adopted by existing service registries usually suffers from low recall and is insufficient to retrieve accurate RESTful services according to users’ functional goals. Moreover, it is often difficult for users to specify queries that can precisely represent their requirements due to the lack of knowledge on their desired service functionalities. Toward these issues, we propose a RESTful service discovery approach by leveraging service goal (i.e., service functionality) knowledge mined from services’ textual descriptions. The approach first groups the available services into clusters using probabilistic topic models. Then, service goals are extracted from the textual descriptions of services and also clustered based on the topic modeling results of services. Based on service goal clusters, we design a mechanism to recommend semantically relevant service goals to help users refine their initial queries. Relevant services are retrieved by matching user selected service goals with those of candidate services. To improve the recall of the goal-based service discovery approach, we further propose a hybrid approach by integrating it with two existing service discovery approaches. A series of experiments conducted on real-world services crawled from a publicly accessible registry, ProgrammableWeb, demonstrate the effectiveness of the proposed approaches.

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Notes

  1. http://www.programmableweb.com/.

  2. http://wordnet.princeton.edu/.

  3. http://www.nltk.org/.

  4. http://jgibblda.sourceforge.net/.

  5. http://nlp.stanford.edu/software/lex-parser.shtml.

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Acknowledgements

This research was supported by the National Basic Research Program of China (No. 2014CB340404), the National Key Research and Development Program of China (No. 2017YFB1400602), and the National Natural Science Foundation of China (Nos. 61672387, 61702378, 61402150, and 61562073), the Strategic Team-Building of Scientific and Technological Innovation in Hubei Province, and the Natural Science Foundation of Hubei Province of China (No. 2017CKB894).

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Authors and Affiliations

  1. State Key Lab of Software Engineering, Computer School, Wuhan University, Wuhan, China

    Neng Zhang, Jian Wang & Keqing He

  2. School of Computer and Information Engineering, Henan University, Kaifeng, China

    Zheng Li

  3. School of Data Science, Tongren University, Tongren, China

    Yiwang Huang

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  1. Neng Zhang
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Correspondence to Jian Wang.

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Zhang, N., Wang, J., He, K. et al. Mining and clustering service goals for RESTful service discovery. Knowl Inf Syst 58, 669–700 (2019). https://doi.org/10.1007/s10115-018-1171-4

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