Chapter 3 described how a many-valued approach can be combined with multiquerying to address problems of imprecision. The discussions now turn to the subject of inferencing. SMS generates two kinds of inferences that correspond to deductive inferences and nondeducdve inferences. Derivation processes use these inferences, along with the relation of identity and relations between sentential expressions and their corresponding expansions and normal forms, to produce both unqualified and qualified proofs. In SMS, proof of a conclusion amounts to demonstrating its ‘presence’ in the premises (database). The notion of presence is specially defined to suit the needs of a conversational mode of expression in which queries are taken as conclusions to be proved. A ‘conclusion’ is merely a well formed sentential sequence. It is proved by demonstrating that its components are present in the database and that they hold the same or equivalent relationships with one another in the database as they do in the conclusion to be proved.


Normal Form Linguistic Variable Modal Qualifier Derivation Process Derivability Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1991

Authors and Affiliations

  • Cary G. deBessonet
    • 1
    • 2
  1. 1.AI ProjectLouisiana State Law InstituteUSA
  2. 2.Southern UniversityUSA

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