Computational semiotics is an interdisciplinary field that applies, conducts, and draws on research in logic, mathematics, the theory and practice of computation, formal and natural language studies, the cognitive sciences generally, and semiotics proper. A common theme of this work is the adoption of a sign-theoretic perspective on issues of artificial intelligence and knowledge representation. Many of its applications lie in the field of computer-human interaction (CHI) and the fundamental devices of recognition (work at IASE in California).

Computational semiotics is that branch of one,which deals with the study and application of logic,computation to formal,natural language in terms of cognition and signs.

One part of this field, known as algebraic semiotics, combines aspects of algebraic specification and social semiotics, and has been applied to user interface design and to the representation of mathematical proofs.

Computational Semiotics by Gudwin

Fig below (http://www.dca.fee.unicamp.br/~gudwin/compsemio/Image24.gif):

Singularities captured from real world.

Gudwin describes knowlege as knowledge units (see figure above).

Computational Semiotics vs Symbol-Driven Engineering

SDE expresses phenomena using symbols. There are interpretations between symbols, expressed in predicates.

The classification of knowledge units by Gudwin is below (the origin is from Charles Peirce):

Classification of knowledge units.

First, source code fits well to the branch of the tree, which starts from the node rhematic. Second, when code is considered as sequences or using traces, then the dicent approach is relevant.  Third, when some features of the code or its assumed behavior is considered, then the approach argumentative is relevant.

Symbolic analysis is then a tuple (rhematic, dicent, argumentative).

Mastering these three branches of the knowledge tree gives possibilities to master source code.

Some links:

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