Expert knowledge ontological modeling for the analyse of abnormal behaviour : application for maritime surveillance

In our societies, where information is everywhere the capacity to capture and analyze this information is a major issue for every entity that must take a decision. From this capacity dependsthe efficiency of the actions and measures that will be undertaken. But face with the large increase of available data, the increasing number of actors and the increasing complexity of threats the strategic environment in which decision-makers operates is characterized by a significant uncertainty. This general observation is also present in the maritime domain characterised by heterogeneous threats, a large effective area and a large number of stakeholders.Therefore, this proposed research explores the potentiality of spatial ontologies for modelling, sharing and inference. The purpose is to provide a suitable environment for the experts where knowledge about abnormal maritime behaviour can be modelled. However, this knowledge is spatio-temporal. It has therefore been necessary to extend the initial functionality of the SWRL language to take into account these characteristics.Finally, the adopted approach has been had been implemented inside the OntoMap prototype. From retrieving the data to the cartographic analysis, this prototype offers all the necessary elements to understand an abnormal situation.

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Field Value
Source https://pastel.hal.science/pastel-00819259
Author Vandecasteele, Arnaud
Maintainer CCSD
Last Updated May 11, 2026, 06:32 (UTC)
Created May 11, 2026, 06:32 (UTC)
Identifier NNT: 2012ENMP0077
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Centre de recherche sur les Risques et les Crises (CRC) ; Mines Paris - PSL (École nationale supérieure des mines de Paris) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)
creator Vandecasteele, Arnaud
date 2012-10-30T00:00:00
harvest_object_id 030b6690-73f7-4f0c-bcf9-ceb4221e1b8e
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE