A model for an imperfect knowledge base for high-level information fusion experiments

Evaluation of fusion algorithms constitutes a pivotal phase in the life-cycle of designing solutions for fusion problems. Benchmark datasets together with standardized evaluation criteria and metrics are thus needed to run proper experiments. When it comes to higher-level tasks such as situation assessment, datasets are conspicuously absent. Among the reasons are the lack of formal characterization of situation assessment, and the lack of structure to guide the collection of data at an appropriate level of semantics and granularity. In this paper, we propose a model for an imperfect knowledge base (IKB) to support high-level information fusion experiments. The model includes characterization of imperfect information from partially reliable sources. The concept of infon is an elementary piece of information which can bear on both concrete and abstract objects. The corresponding knowledge base encodes infons about physical objects of interest in a situation (e.g., a vessel), about sources providing infons, and about other infons. The imperfection dimensions of information are captured and connected to reliability dimensions of the source. An implementation within a graph database is proposed, exemplified with data of a maritime scenario. An example of comparison of artificial and human agents executing a fusion task is shown. Future work are finally briefly discussed.