Context-sensitive Web service discovery over the bipartite graph model

doi:10.1007/s11704-013-1256-x

Front Comput Sci

0, Vol.

Issue () : 875-893 https://doi.org/10.1007/s11704-013-1256-x

Context-sensitive Web service discovery over the bipartite graph model

Rong ZHANG¹(

), Koji ZETTSU²(

), Yutaka KIDAWARA², Yasushi KIYOKI^2,3(

), Aoying ZHOU¹

1. Software Engineering Institute, East China Normal University, Shanghai 200062, China; 2. National Institute of Information and Communications Technology, Kyoto 619-0289, Japan; 3. Keio University, Kanagawa 252-8520, Japan

Download: PDF(1021 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

As service oriented architecture (SOA) matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/or desired goals of users. This leads to the notion that service discovery should take the “usage context” of service into account as well as service content (descriptions) which have been well explored. In this paper, we introduce the concept of service context which is used to represent service usage. In query processing, both service content and service context are examined to identify services. We propose to represent service context by a weighted bipartite graph model. Based on the bipartite graph model, we reduce the gap between query space and service space by query expansion to improve recall. We also design an iteration algorithm for result ranking by considering service context-usefulness as well as content-relevance to improve precision. Finally, we develop a service search engine implementing this mechanism, and conduct some experiments to verify our idea.

Keywords Web service usage context bipartite graph model context-usefulness

Corresponding Author(s): ZHANG Rong,Email:rzhang,ayzhou@sei.ecnu.edu.cn; ZETTSU Koji,Email:zettsu,kidawara@nict.go.jp; KIYOKI Yasushi,Email:kiyoki@sfc.keio.ac.jp

Issue Date: 01 December 2013

Cite this article:

Rong ZHANG,Koji ZETTSU,Yutaka KIDAWARA, et al. Context-sensitive Web service discovery over the bipartite graph model[J]. Front Comput Sci, 0, (): 875-893.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-013-1256-x
https://academic.hep.com.cn/fcs/EN/Y0/V/I/875

1	Fan J, Kambhampati S. A snapshot of public web services. Journal of the ACM SIGMOD Record , 2005, 34(1): 24-32 doi: 10.1145/1058150.1058156
2	Xu J, Croft W. Improving the effectiveness of information retrieval with local context analysis. ACM Transactions on Information Systems , 2000, 18(1): 79-112 doi: 10.1145/333135.333138
3	Dong X, Halevy A, Madhavan J, Nemes E, Zhang J. Similarity search for web services. In: Proceedings of VLDB . 2004, 372-383
4	Haveliwala T H. Topic-sensitive pagerank. In: Proceedings of WWW . 2002, 517-526
5	Page L, Brin S, Motwani R, Winograd, T. The PageRank citation ranking: bringing order to the Web. Stanford Digital Libraries Working Paper , 1998
6	Zhang R, Zettsu K, Kidawara Y, Kiyoki Y. Context-sensitive query expansion over the bipartite graph model forweb service search. In: Proceedings of DASFAA . 2011, 418-433
7	Morris M R, Teevan J. Enhancing collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting. In: Proceedings of CSCW . 2008, 481-484
8	Medjahed B, Atif Y. Context-based matching for web service composition. Distributed and Parallel Databases , 2007, 21(1): 5-37 doi: 10.1007/s10619-006-7003-7
9	Erl T. Service-oriented architecture: a field guide to integrating XML and Web services. Upper Saddle River , NJ, USA: Prentice Hall, 2004
10	Ankolekar A, Burstein M, Hobbs J R, Lassila O, Martin D, McDermott D, McIlraith S A, Narayanan S, Paolucci M, Payne T. Daml-S: Web service description for the semantic web. In: Proceedings of ISWC . 2002, 348-363
11	Roman D, Keller U, Lausen H, De Bruijn J, Lara R, Stollberg M, Polleres A, Feier C, Bussler C, Fensel D. Web service modeling ontology. Journal Applied Ontology , 2005, 1(1): 77-106
12	Pautasso C, Zimmermann O, Leymann F. RESTful Web services vs. “big” Web services: making the right architectural decision. In: Proceedings of WWW . 2008, 805-814
13	Plebani P, Pernici B. Urbe: Web service retrieval based on similarity evaluation. IEEE Transactions on Knowledgement and Data Engineering , 2009, 21(11): 1629-1642 doi: 10.1109/TKDE.2009.35
14	Kleinberg J. Authoritative sources in a hyperlinked environment. Journal of the ACM , 1999 doi: 10.1145/324133.324140
15	Sebastiani F. Text categorization. Text Mining and its Applications , 2005, 109-129
16	Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing and Management , 1998, 24(5): 513-523 doi: 10.1016/0306-4573(88)90021-0
17	Mitchell T. Machine Learning. Boston: McGraw-Hill, 1997
18	Vectomova O, Wang Y. A study of the effect of term proximity on query expansion. Journal of Information Science , 2006, 32(4): 324-333 doi: 10.1177/0165551506065787
19	Hsu W H, Chang S F. Topic tracking across broadcast news videos with visual duplicates and semantic concepts. In: Proceedings of ICIP . 2006
20	Bourbaki N. Topological Vector Spaces. Springer , 1987 doi: 10.1007/978-3-642-61715-7
21	Liu L, Sun L, Rui Y, Shi Y, Yang S Q. Web video topic discovery and tracking via bipartite graph reinforcement model. In: Proceedings of WWW . 2008, 1009-1018
22	Salton G, McGill M J. Introduction to Modern Information Retrieval. McGraw-Hill , 1986
23	Yom-Tov E, Fine S, Carmel D, Darlow A. Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval. In: Proceedings of SIGIR . 2005, 512-519
24	Voorhees E, Harman D. Overview of the sixth text retrieval conference (TREC-6). Information Processing & Management , 2000, 36(1): 3-35 doi: 10.1016/S0306-4573(99)00043-6
25	Guo R, Chen D, Le J. Matching semantic web services across heterogeneous ontologies. In: Proceedings of CIT . 2005, 264-268
26	Wong J, Hong J I. Making mashups with marmite: towards end-user programming for the web. In: Proceedings of CHI . 2007, 1435-1444
27	Lee C, Helal S. Context attributes: an approach to enable contextawareness for service discovery. In: Proceedings of SAINT . 2003, 22-30
28	Segev A, Toch E. Context-based matching and ranking of web services for composition. IEEE Transactions on Services Computing , 2009, 2(3): 210-222 doi: 10.1109/TSC.2009.14
29	Yang Y, Mahon F, Willams M H, Pfeifer T. Context-aware dynamic personalised service re-composition in a pervasive service environment. In: Proceedings of UIC . 2006, 724-735
30	Bellur U, Kulkarni R. Improved matchmaking algorithm for semantic web services based on bipartite graph matching. In: Proceedings of ICWS . 2007, 86-93
31	Langville A, Meyer C. Google’s PageRank and Beyond: the Science of Search Engine Rankings. Princeton University Press , 2006

[1]	Jianpeng HU, Linpeng HUANG, Tianqi SUN, Ying FAN, Wenqiang HU, Hao ZHONG. Proactive planning of bandwidth resource using simulation-based what-if predictions forWeb services in the cloud[J]. Front. Comput. Sci., 2021, 15(1): 151201-.
[2]	Xiaodong FU,Kun YUE,Li LIU,Ping ZOU,Yong FENG. Discovering admissibleWeb services with uncertain QoS[J]. Front. Comput. Sci., 2015, 9(2): 265-279.
[3]	Hadi SABOOHI, Sameem ABDUL KAREEM. An automatic subdigraph renovation plan for failure recovery of composite semantic Web services[J]. Front. Comput. Sci., 2013, 7(6): 894-913.
[4]	Hongli YANG, Chao CAI, Liyang PENG, Xiangpeng ZHAO, Zongyan QIU, Shengchao QIN. Algorithms for checking channel passing in web service choreography[J]. Front. Comput. Sci., 2013, 7(5): 710-728.
[5]	Xiaoqin FAN, Xianwen FANG, Zhijun DING. Indeterminacy-aware service selection for reliable service composition[J]. Front Comput Sci Chin, 2011, 5(1): 26-36.

Viewed

Full text

Abstract

Cited

Shared

Discussed