Flexible rule-based inference exploiting taxonomies

Rule-based systems are widely used to implement knowledge-based systems. They are usually intuitive to use, have good performance and can be easily integrated with other software components. However, a critical problem is that the behavior of a rule-based system tends to degrade abruptly whenever the knowledge base is incomplete or not detailed enough or when operating at the borders of its expertise. Various forms of approximate reasoning have been introduced but they solve the problem only in a partial way. In the paper we propose new forms of rule inference that tackle this problem, introducing a form of flexible or common sense reasoning that can support a softer degradation of problem solving ability when knowledge is partial or incomplete. The solution we propose relies on the exploitation of semantic information associated with the concepts involved in the rules. In particular, we show how taxonomical information can be exploited to define flexible forms of match between rule antecedents and the working memory, and flexible forms of conflict resolution. In this way, even when no rule perfectly matches the working memory, the inference engine can select rules that apply to more general or to similar cases and provide some approximate solution. The approach has been motivated by work on context aware (recommender) systems where the problem of incomplete descriptions and brittle degradation of problem solving ability are particularly relevant.