High-level fusion based on conceptual graphs

Most of studies in the field of information fusion focus on the production of high-level information from low-level data. The challenge is then to fuse this high-level information to produce a global and coherent information. Another approach consists in interpreting data as high-level information and fuse it at once. Our approach relies on the use of conceptual graphs model. The model is widely used for knowledge representation. We propose to go further and use it for information fusion. Conceptual graphs model contains aggregation operators such as join and maximal join. This paper is dedicated to the extension of the maximal join operator in order to manage heterogeneous information fusion. After describing the suitability of maximal join for high-level information fusion, we present the extension that we propose. The extension relies on relaxing the equality constraint on observations and on using fusion strategies. A case study illustrates our proposition.