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Mining and clustering service goals for RESTful service discovery.

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Title: Mining and clustering service goals for RESTful service discovery.
Authors: Zhang, Neng1 nengzhang@whu.edu.cn, Wang, Jian1 jianwang@whu.edu.cn, He, Keqing1 hekeqing@whu.edu.cn, Li, Zheng2 zhengli_hope@whu.edu.cn, Huang, Yiwang3 huangyw@whu.edu.cn
Source: Knowledge & Information Systems. Mar2019, Vol. 58 Issue 3, p669-700. 32p.
Subjects: Querying (Computer science), Database searching, Search algorithms, Keyword searching, Information storage & retrieval systems
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. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Neng%22">Zhang, Neng</searchLink><relatesTo>1</relatesTo><i> nengzhang@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jian%22">Wang, Jian</searchLink><relatesTo>1</relatesTo><i> jianwang@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Keqing%22">He, Keqing</searchLink><relatesTo>1</relatesTo><i> hekeqing@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zheng%22">Li, Zheng</searchLink><relatesTo>2</relatesTo><i> zhengli_hope@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Yiwang%22">Huang, Yiwang</searchLink><relatesTo>3</relatesTo><i> huangyw@whu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. Mar2019, Vol. 58 Issue 3, p669-700. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Querying+%28Computer+science%29%22">Querying (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Database+searching%22">Database searching</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Keyword+searching%22">Keyword searching</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Search algorithms
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      – SubjectFull: Keyword searching
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              Text: Mar2019
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