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Link Prediction on Dynamic Attributed Knowledge Graphs for Maritime Situational Awareness

Jacques Everwyn 1, 2 Abdel-Illah Mouaddib Bruno Zanuttini Sylvain Gatepaille Stephan Brunessaux
1 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Currently, maritime surveillance operators have to monitor by hand the massive amount of data at their disposal to spot the events of interest, thus limiting their capabilities. Maritime data comes from various and heterogeneous sources, that can be merged into a dynamic attributed knowledge graph which represents an evolving maritime situation. Using this graph, the automation of alert rising comes through a link prediction task: given some labels from expert knowledge, are there similar situations of interest elsewhere in the graph? In this article, we review link prediction techniques for situation awareness in a maritime context, and draw conclusions on how the addition of attributes in a dynamic graph model could improve results on this task.
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Jacques Everwyn, Abdel-Illah Mouaddib, Bruno Zanuttini, Sylvain Gatepaille, Stephan Brunessaux. Link Prediction on Dynamic Attributed Knowledge Graphs for Maritime Situational Awareness. Conférence Nationale sur les Applications Pratiques de l’Intelligence Artificielle (APIA 2019), Jul 2019, Toulouse, France. ⟨hal-02942859⟩

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