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Conference Papers Year : 2017

Semantic Web Datatype Inference: Towards Better RDF Matching


In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we propose a datatype inference process based on four steps: (i) predicate information analysis (i.e., deduce the datatype from existing range property); (ii) analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical-space); (iii) semantic analysis of the predicate name and its context; and (iv) generalization of numeric and binary datatypes to ensure the integration. We evaluated the performance and the accuracy of our approach with datasets from DBpedia. Results show that the execution time of the inference process is linear and its accuracy can increase up to 97.10%. \textcopyright 2017, Springer International Publishing AG.
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hal-01909097 , version 1 (23-01-2020)



Irvin Dongo, Yudith Cardinale, Firas Al-Khalil, Richard Chbeir. Semantic Web Datatype Inference: Towards Better RDF Matching. Web Information Systems Engineering - WISE 2017 - 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part II, Oct 2017, Puschino, Russia. pp.57-74, ⟨10.1007/978-3-319-68786-5_5⟩. ⟨hal-01909097⟩


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