In this chapter, the application of the logic to knowledge representation is studied. The main idea is the identification of a class of terms, called basic terms, suitable for representing individuals in diverse applications. For example, this class is suitable for machine-learning applications. From a (higherorder) programming language perspective, basic terms are data values. The most interesting aspect of the class of basic terms is that it includes certain abstractions and therefore is wider than is normally considered for knowledge representation. These abstractions allow one to model sets, multisets, and data of similar types, in a direct way. Of course, there are other ways of introducing (extensional) sets, multisets, and so on, without using abstractions. For example, one can define abstract data types or one can introduce data constructors with special equality theories. The primary advantage of the approach adopted here is that one can define these abstractions intensionally, as shown in Chap. 5. Techniques for defining metrics and kernels on basic terms are also investigated in this chapter.
KeywordsCovariance Prefix Bromine
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