An Approach to Extraction of Linguistic Recommendation Rules - Application of Modal Conditionals Grounding

An approach to linguistic summarization of distributed databases is considered. It is assumed that summarizations are produced for the case of incomplete access to existing data. To cope with the problem the stored data are processed partially (sampled). In consequence summarizations become equivalent to the natural language modal conditionals with modal operators of knowledge, belief and possibility. To capture this case of knowledge processing an original theory for grounding of modal languages is applied. Simple implementation scenarios and related computational techniques are suggested to illustrate a possible utilization of this model of linguistic summarization.