LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus

Abstract : The ever increasing need for extracting knowledge from heterogeneous data has become a major concern. This is particularly observed in many application domains where several actors, with different expertise, exchange a great amount of information at any stage of a large-scale project. In this paper, we propose LinkedMDR: a novel ontology for Linked Multimedia Document Representation that describes the knowledge of a heterogeneous document corpus in a semantic data network. LinkedMDR combines existing standards and introduces new components that handle the connections between these standards and augment their capabilities. It is generic and offers a pluggable layer that makes it adaptable to different domain-specific knowledge. Experiments conducted on construction projects show that LinkedMDR is applicable in real-world scenarios.
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Communication dans un congrès
The 28th International Conference on Database and Expert Systems Applications (DEXA 2017), Aug 2017, Lyon, France. Springer LNCS, 10438, pp.362-377, 2017, Database and Expert Systems Applications: 28th International Conference (DEXA 2017). 〈http://www.dexa.org/previous/dexa2017/dexa2017.html〉. 〈10.1007/978-3-319-64468-4_28〉
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https://hal.archives-ouvertes.fr/hal-01817333
Contributeur : Sébastien Laborie <>
Soumis le : dimanche 17 juin 2018 - 12:04:32
Dernière modification le : mardi 6 novembre 2018 - 01:26:02

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Nathalie Charbel, Christian Sallaberry, Sébastien Laborie, Gilbert Tekli, Richard Chbeir. LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus. The 28th International Conference on Database and Expert Systems Applications (DEXA 2017), Aug 2017, Lyon, France. Springer LNCS, 10438, pp.362-377, 2017, Database and Expert Systems Applications: 28th International Conference (DEXA 2017). 〈http://www.dexa.org/previous/dexa2017/dexa2017.html〉. 〈10.1007/978-3-319-64468-4_28〉. 〈hal-01817333〉

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