Achieving maritime situational awareness using knowledge graphs: a study

Jacques Everwyn (1, 2) , Bruno Zanuttini (3) , Abdel-Illah Mouaddib (3) , Sylvain Gatepaille (2) , Stephan Brunessaux (2)

1 Equipe MAD - Laboratoire GREYC - UMR6072
2 Airbus Defence and Space [Les Mureaux]
3 UNICAEN - Université de Caen Normandie

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.

Mots clés: attributes; machine learning; link prediction; Dynamic knowledge graph; maritime situation

Domaines: Intelligence artificielle [cs.AI]

Jacques Everwyn, Bruno Zanuttini, Abdel-Illah Mouaddib, Sylvain Gatepaille, Stephan Brunessaux. Achieving maritime situational awareness using knowledge graphs: a study. 1st Maritime Situational Awareness Workshop (MSAW 2019), Oct 2019, Lerici, Italy. ⟨hal-02942856⟩