Abstract
This paper presents an efficient approach to query big RDF datasources in order to get more relevant and complete results. The approach deals with two important heterogeneities in huge amount of data: semantic and URI-based entity identification heterogeneities. The paper proposes: (1) a semantic entity resolution approach based on inference mechanism to manage ambiguity of real world entities for linking data at the semantic and URI levels (2) a MapReduce-based query rewriting approach based on entity resolution results to include implicit data into query results (3) algorithms based on MapReduce paradigm to deal with huge amounts of data.
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References
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Acknowledgment
The research leading to these results has received funding for Square Predict project from Fonds National pour la Société Numrique (FSN)- project investissement d’avenir (PIA 2013) Cloud Computing n 3 - Big Data program.
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Benbernou, S., Huang, X., Ouziri, M. (2015). Fusion of Big RDF Data: A Semantic Entity Resolution and Query Rewriting-Based Inference Approach. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_27
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DOI: https://doi.org/10.1007/978-3-319-26187-4_27
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