Executing, Comparing, and Reusing Linked Data-Based Recommendation Algorithms With the Allied Framework

Cristhian Figueroa 1 Iacopo Vagliano 1 Oscar Rodríguez Rocha 2 Marco Torchiano 1 Catherine Faron Zucker 3 Juan Carlos Corrales 4 Maurizio Morisio 1
1 DAUIN - Dipartimento di Automatica e Informatica [Torino]
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
4 Universidad del Cauca [Popayán]
Abstract : Data published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.
Keywords : Interlinked Data DBpedia Web of data Evaluation Framework Recommender System Structure Semantic Recommendation Linked Data datasets
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Contributor : Oscar Rodríguez Rocha <>
Submitted on : Friday, December 7, 2018 - 11:04:39 AM
Last modification on : Thursday, February 7, 2019 - 4:55:08 PM
Long-term archiving on: Friday, March 8, 2019 - 12:51:12 PM

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Cristhian Figueroa, Iacopo Vagliano, Oscar Rodríguez Rocha, Marco Torchiano, Catherine Faron Zucker, et al.. Executing, Comparing, and Reusing Linked Data-Based Recommendation Algorithms With the Allied Framework. Semantic Web Science and Real-World Applications, IGI Global, pp.18-47, 2019, 9781522571865. ⟨10.4018/978-1-5225-7186-5⟩. ⟨hal-01939482⟩

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