MidSemI: A Middleware for Semantic Integration of Business Data with Large-scale Social and Linked Data

Samir Sellami, Taoufiq Dkaki, Nacer Eddine Zarour and Pierre-Jean Charrel
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Samir Sellami: LIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria
Taoufiq Dkaki: IRIT Laboratory, University of Toulouse 2 - Jean Jaurès, Toulouse, France
Nacer Eddine Zarour: LIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria
Pierre-Jean Charrel: IRIT Laboratory, University of Toulouse 2 - Jean Jaurès, Toulouse, France

International Journal of Information System Modeling and Design (IJISMD), 2019, vol. 10, issue 2, 1-25

Abstract: The web diversification into the Web of Data and social media means that companies need to gather all the necessary data to help make the best-informed market decisions. However, data providers on the web publish data in various data models and may equip it with different search capabilities, thus requiring data integration techniques to access them. This work explores the current challenges in this area, discusses the limitations of some existing integration tools, and addresses them by proposing a semantic mediator-based approach to virtually integrate enterprise data with large-scale social and linked data. The implementation of the proposed approach is a configurable middleware application and a user-friendly keyword search interface that retrieves its input from internal enterprise data combined with various SPARQL endpoints and Web APIs. An evaluation study was conducted to compare its features with recent integration approaches. The results illustrate the added value and usability of the contributed approach.

Date: 2019
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