On the consideration of a bring-to-mind model for computing the Information Content of concepts defined into ontologies

Ontologies are core elements of numerous applications that are based on computer-processable expert knowledge. They can be used to estimate the Information Content (IC) of the key concepts of a domain: a central notion on which depend various ontology-driven analyses, e.g. semantic measures. This paper proposes new IC models based on the belief functions theoretical framework. These models overcome limitations of existing ICs that do not consider the inductive inference assumption intuitively assumed by human operators, i.e. that occurrences of a concept (e.g. Maths) not only impact the IC of more general concepts (e.g. Sciences), as considered by traditional IC models, but also the one of more specific concepts (e.g. Algebra). Interestingly, empirical evaluations show that, in addition to modelling the aforementioned assumption, proposed IC models compete with best state-of-the-art models in several evaluation settings.