A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains

In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environments.

[1]  Ramesh Sharda,et al.  Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis , 2019, Information Systems Frontiers.

[2]  Kai Xu,et al.  A web services-based approach to develop a networked information integration service platform for gear enterprise , 2012, J. Intell. Manuf..

[3]  Yang Guo,et al.  A survey of collaborative filtering based social recommender systems , 2014, Comput. Commun..

[4]  Giuseppe M. L. Sarnè,et al.  A Multi-tiered Recommender System Architecture for Supporting E-Commerce , 2012, IDC.

[5]  José Manuél Gómez-Pérez,et al.  When History Matters - Assessing Reliability for the Reuse of Scientific Workflows , 2013, International Semantic Web Conference.

[6]  Hanh Huu Hoang,et al.  Retracted: Semantic Information Integration with Linked Data Mashups Approaches , 2014, Int. J. Distributed Sens. Networks.

[7]  Manuel de Buenaga Rodríguez,et al.  Tourist Face: A Contents System Based on Concepts of Freebase for Access to the Cultural-Tourist Information , 2011, NLDB.

[8]  Boris Villazón-Terrazas,et al.  Controlling and Monitoring Crisis , 2015, ESWC.

[9]  Weiming Shen,et al.  An agent-based service-oriented integration architecture for collaborative intelligent manufacturing , 2007 .

[10]  Enrico Blanzieri,et al.  Implicit: a multi-agent recommendation system for web search , 2010, Autonomous Agents and Multi-Agent Systems.

[11]  Dickson K. W. Chiu,et al.  Towards ubiquitous tourist service coordination and process integration: A collaborative travel agent system architecture with semantic web services , 2009, Inf. Syst. Frontiers.

[12]  Axel Polleres,et al.  Enabling Web-scale data integration in biomedicine through Linked Open Data , 2019, npj Digital Medicine.

[13]  Mary Roth,et al.  Information integration: A new generation of information technology , 2002, IBM Syst. J..

[14]  Hamid Pirahesh,et al.  Information integration: A research agenda , 2002, IBM Syst. J..

[15]  Russ B. Altman,et al.  Bioinformatics challenges for personalized medicine , 2011, Bioinform..

[16]  Antonio Moreno,et al.  Intelligent tourism recommender systems: A survey , 2014, Expert Syst. Appl..

[17]  Laura M. Haas,et al.  Information integration in the enterprise , 2008, CACM.

[18]  Stefan Voß,et al.  A Cloud-Based SOA for Enhancing Information Exchange and Decision Support in ITT Operations , 2014, ICCL.

[19]  Ngamnij Arch-int,et al.  Semantic Interoperability for Data Integration Framework using Semantic Web Services and Rule-based Inference: A case study in healthcare domain , 2013 .

[20]  Joseph A. Konstan,et al.  Introduction to recommender systems , 2008, SIGMOD Conference.

[21]  Manuel de Buenaga Rodríguez,et al.  An intelligent information access system assisting a case based learning methodology evaluated in higher education with medical students , 2012, Comput. Educ..

[22]  Florentino Fernández Riverola,et al.  Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects , 2017, Int. J. Medical Informatics.

[23]  S. Jamieson.  Likert scales: how to (ab)use them , 2004, Medical education.

[24]  Munindar P. Singh,et al.  Generalized framework for personalized recommendations in agent networks , 2012, Autonomous Agents and Multi-Agent Systems.

[25]  Hye-Young Paik,et al.  Data integration in mashups , 2009, SGMD.

[26]  Pornpit Wongthongtham,et al.  State of the art of a multi-agent based recommender system for active software engineering ontology , 2013 .

[27]  Li Chen,et al.  Evaluating recommender systems from the user’s perspective: survey of the state of the art , 2012, User Modeling and User-Adapted Interaction.

[28]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[29]  Dale Goodhue,et al.  Understanding user evaluations of information systems , 1995 .

[30]  Laura M. Haas,et al.  Beauty and the Beast: The Theory and Practice of Information Integration , 2007, ICDT.

[31]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[32]  Thomas Lukasiewicz,et al.  Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+/- , 2013, URSW.

[33]  Hong Joo Lee,et al.  Use of social network information to enhance collaborative filtering performance , 2010, Expert Syst. Appl..

[34]  Steffen Staab,et al.  What Is an Ontology? , 2009, Handbook on Ontologies.

[35]  Philip David Smart,et al.  Multi-source Toponym Data Integration and Mediation for a Meta-Gazetteer Service , 2010, GIScience.

[36]  Rafael Valencia-García,et al.  Solving the cold-start problem in recommender systems with social tags , 2010, Expert Syst. Appl..