Information Fusion using Conceptual Graphs: a TV Programs Case Study

On the one hand, Conceptual Graphs are widely used in natural language processing systems. On the other hand, information fusion community lacks of tools and methods for knowledge representation. Using natural language processing techniques for information fusion is a new field of interest in the fusion community. Our aim is to take the advantage of both communities and propose a framework for high-level 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. Domain knowledge has to be injected into the maximal join operation in order to satisfy the constraints of fusion. The extension relies on relaxing the equality constraint on observations and on using fusion strategies. A case study illustrates our proposition and we describe the experimentations that we conducted in order to validate our approach.