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LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus
Nathalie Charbel, Christian Sallaberry, Sébastien Laborie, Gilbert Tekli, Richard Chbeir

LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus

Science & Mathematics Published 2017 Verification Pending

Overview & Synopsis

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.

Indexed from repository: HAL Open Science (hal.science)
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Document Information

Document Title LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus
Author / Organization Nathalie Charbel, Christian Sallaberry, Sébastien Laborie, Gilbert Tekli, Richard Chbeir
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2017
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-199824
Original Source Link Visit Source Page on hal.science »