Exploiting User Tagging for Web Service Co-Clustering

We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process o...
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Autor*in:	Tingting Liang [verfasserIn] Yishan Chen [verfasserIn] Wei Gao [verfasserIn] Ming Chen [verfasserIn] Meilian Zheng [verfasserIn] Jian Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Web service WSDL documents clustering co-clustering tag recommendation

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 168981-168993
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:168981-168993

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2950355

Katalog-ID:	DOAJ05697597X

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allfields_unstemmed	10.1109/ACCESS.2019.2950355 doi (DE-627)DOAJ05697597X (DE-599)DOAJ7b11732e8b9b45ca8bf4a2a5824ea221 DE-627 ger DE-627 rakwb eng TK1-9971 Tingting Liang verfasserin aut Exploiting User Tagging for Web Service Co-Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset. Web service WSDL documents clustering co-clustering tag recommendation Electrical engineering. Electronics. Nuclear engineering Yishan Chen verfasserin aut Wei Gao verfasserin aut Ming Chen verfasserin aut Meilian Zheng verfasserin aut Jian Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 168981-168993 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:168981-168993 https://doi.org/10.1109/ACCESS.2019.2950355 kostenfrei https://doaj.org/article/7b11732e8b9b45ca8bf4a2a5824ea221 kostenfrei https://ieeexplore.ieee.org/document/8892516/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 168981-168993
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allfieldsSound	10.1109/ACCESS.2019.2950355 doi (DE-627)DOAJ05697597X (DE-599)DOAJ7b11732e8b9b45ca8bf4a2a5824ea221 DE-627 ger DE-627 rakwb eng TK1-9971 Tingting Liang verfasserin aut Exploiting User Tagging for Web Service Co-Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset. Web service WSDL documents clustering co-clustering tag recommendation Electrical engineering. Electronics. Nuclear engineering Yishan Chen verfasserin aut Wei Gao verfasserin aut Ming Chen verfasserin aut Meilian Zheng verfasserin aut Jian Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 168981-168993 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:168981-168993 https://doi.org/10.1109/ACCESS.2019.2950355 kostenfrei https://doaj.org/article/7b11732e8b9b45ca8bf4a2a5824ea221 kostenfrei https://ieeexplore.ieee.org/document/8892516/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 168981-168993
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source	In IEEE Access 7(2019), Seite 168981-168993 volume:7 year:2019 pages:168981-168993
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topic_facet	Web service WSDL documents clustering co-clustering tag recommendation Electrical engineering. Electronics. Nuclear engineering
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callnumber-first	T - Technology
author	Tingting Liang
spellingShingle	Tingting Liang misc TK1-9971 misc Web service misc WSDL documents clustering misc co-clustering misc tag recommendation misc Electrical engineering. Electronics. Nuclear engineering Exploiting User Tagging for Web Service Co-Clustering
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topic_title	TK1-9971 Exploiting User Tagging for Web Service Co-Clustering Web service WSDL documents clustering co-clustering tag recommendation
topic	misc TK1-9971 misc Web service misc WSDL documents clustering misc co-clustering misc tag recommendation misc Electrical engineering. Electronics. Nuclear engineering
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title	Exploiting User Tagging for Web Service Co-Clustering
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title_auth	Exploiting User Tagging for Web Service Co-Clustering
abstract	We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset.
abstractGer	We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset.
abstract_unstemmed	We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset.
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title_short	Exploiting User Tagging for Web Service Co-Clustering
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