A Conceptual Architecture for Semantic Mash Up Recommender Framework

In this paper we propose a conceptual semantic based tourism platform that enables users to consume mash up applications by using a multi agent system. We explore different mechanisms for discovery, publishing, composition and querying of services. In particular, we present a methodology for collecting, organizing and searching data distributed in different resources-RDF data sets- and - Application Programming Interface (API) -. As a part of our model, we propose a semantic architecture that defines three main components: i) A mechanism for automatic data collection. ii) A mechanism for automatic service composition. ii) A semantic recommendation module based on user context and social profile.

[1]  Sandeep Kumar,et al.  Semantic web reasoners and languages , 2011, Artificial Intelligence Review.

[2]  Weiyi Liu,et al.  Discovering semantic associations among Web services based on the qualitative probabilistic network , 2009, Expert Syst. Appl..

[3]  M. Paolucci,et al.  A Broker for OWLS Web services , 2004 .

[4]  Jan Hidders,et al.  Fusion - Visually Exploring and Eliciting Relationships in Linked Data , 2010, International Semantic Web Conference.

[5]  Ubbo Visser,et al.  Finding and Integration of Information-A Practical Solution for the Semantic Web - , 2002 .

[6]  Choochart Haruechaiyasak,et al.  A Semantic Based Question Answering System for Thailand Tourism Information , 2011 .

[7]  A Min Tjoa,et al.  Combining and Integrating Advanced IT-Concepts with Semantic Web Technology Mashups Architecture Case Study , 2010, ACIIDS.

[8]  Thore Graepel,et al.  WWW 2009 MADRID! Track: Data Mining / Session: Statistical Methods Matchbox: Large Scale Online Bayesian Recommendations , 2022 .

[9]  Sean Borman,et al.  The Expectation Maximization Algorithm A short tutorial , 2006 .

[10]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[11]  Charles L. A. Clarke,et al.  Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval , 2007, SIGIR 2007.

[12]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[13]  Yi Zhang,et al.  Efficient bayesian hierarchical user modeling for recommendation system , 2007, SIGIR.

[14]  Wolfgang Nejdl,et al.  From keywords to semantic queries - Incremental query construction on the semantic web , 2009, J. Web Semant..

[15]  Constantin Orasan,et al.  Development and Alignment of a Domain-Specific Ontology for Question Answering , 2008, LREC.

[16]  Richard Scheines,et al.  Bayesian learning of measurement and structural models , 2006, ICML.

[17]  Katia Sycara,et al.  A Broker for OWL-S Web Services , 2004 .

[18]  Michel Riveill,et al.  Web Services Composition: Mashups Driven Orchestration Definition , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.

[19]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[20]  Martin Hepp,et al.  GoodRelations: An Ontology for Describing Products and Services Offers on the Web , 2008, EKAW.

[21]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .