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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Notes
- 1.
Inter-document links are relations between documents. Intra-document links are relations between elements of the same document.
- 2.
- 3.
For the sake of simplicity, we only present 6 documents. However, other documents could be also involved such as videos, audios and 3D drawings.
- 4.
LinkedMDR is an OWL ontology created on Protégé. Details on the LinkedMDR ontology, the overall concepts and relations are available at http://spider.sigappfr.org/linkedmdr/.
- 5.
Available at http://ifcowl.openbimstandards.org/IFC4_ADD2.owl.
- 6.
HIT2GAP (Highly Innovative Building Control Tools) is a large-scale project that involves 21 partners and provides an energy management platform for managing building energy behavior. Further details are available at: http://www.hit2gap.eu/.
- 7.
- 8.
The number of XML tags in the XML annotation files that we generated based on the existing standards and the number of RDF triples that we generated in the LinkedMDR ontology.
- 9.
\(F_2\)-measure: (5Â \(\times \)Â PÂ \(\times \)Â R) /(4Â \(\times \)Â P+Â R)
Recall: No. of covered relevant criteria/Total No. of expected criteria.
Precision: No. of covered relevant criteria/Total No. of annotated criteria.
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Charbel, N., Sallaberry, C., Laborie, S., Tekli, G., Chbeir, R. (2017). LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_28
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