HyperService: Linking and Exploring Services on the Web

Web is becoming a programmable platform, with countless services blooming everyday in various forms like Feeds, REST APIs and Widgets, etc. Although the existing technologies, such as Mashups, have reduced the challenges to build new applications by composing these services, it's still far from enabling the non-technical users to solve their situational problems by correlating and consuming these services. In this paper, we present our HyperService technology, which empowers a much more flexible way to link and explore existing services for solving various situational problems. In HyperService, the service metadata, service linkages and user behaviors are indexed and managed; Based on the user’s input keywords and navigation context, a group of relevant services are dynamically searched, ranked and recommended for facilitating future navigations; the service navigation is smoothed by a web2.0 style exploratory user interface. A prototype system is also presented to demonstrate the effectiveness of our HyperService research work.

[1]  Man Zhang,et al.  Semi-automatically annotating data semantics to web services using ontology mapping , 2008, 2008 12th International Conference on Computer Supported Cooperative Work in Design.

[2]  Gerald Salton,et al.  Automatic text processing , 1988 .

[3]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[4]  C. Lee Giles,et al.  Probabilistic user behavior models , 2003, Third IEEE International Conference on Data Mining.

[5]  Huajun Chen,et al.  Mashup by Surfing a Web of Data APIs , 2009, Proc. VLDB Endow..

[6]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.

[7]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.

[9]  WuZhaohui,et al.  Mashup by surfing a web of data APIs , 2009, VLDB 2009.

[10]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[11]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[12]  Yi Li,et al.  RiMOM: A Dynamic Multistrategy Ontology Alignment Framework , 2009, IEEE Transactions on Knowledge and Data Engineering.

[13]  Amit P. Sheth,et al.  SwetoDblp ontology of Computer Science publications , 2007, J. Web Semant..

[14]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[15]  Umesh Bellur,et al.  Web Service Ranking Using Semantic Profile Information , 2009, 2009 IEEE International Conference on Web Services.

[16]  Rama Akkiraju,et al.  Mashup Advisor: A Recommendation Tool for Mashup Development , 2008, 2008 IEEE International Conference on Web Services.

[17]  Steve Fox,et al.  Evaluating implicit measures to improve web search , 2005, TOIS.

[18]  Zibin Zheng,et al.  WSRec: A Collaborative Filtering Based Web Service Recommender System , 2009, 2009 IEEE International Conference on Web Services.