<abstract xmlns="http://www.w3.org/1999/xhtml">

Compared to existing classical approaches to semiotics which are <italic>dyadic</italic> (signifier/signified, F. de Saussure) and <italic>triadic</italic> (symbol/concept/object, Ch. S. Peirce), this theory can be characterized as <italic>tetradic</italic> ([sign/semion]//[object/noema]) and is the result of either doubling the dyadic approach along the semiotic/ordinary dimension or splitting the ‘concept’ of the triadic one into two (semiotic/ordinary). Other important features of this approach are (a) the distinction made between <italic>concepts</italic> (only functional pairs of extent and intent) and <italic>categories</italic> (as representations of expressions) and (b) the indication of the need for providing the mathematical passage from the <italic>duality between two sets (where one is a singleton) within systems of sets</italic> to <italic>category-theoretical monoids within systems of categories</italic> while waiting for the solution of this problem in the field of logic.
Last but not least, human language expressions are the most representative physical instances of semiotic objects. Moreover, as computational experiments which are possible with linguistic objects present a high degree of systematicity (of oppositions), in general, it is relatively easy to elucidate their dependence on the concepts underlying signs. This new semiotic theory or rather this new research program emerged as the fruit of experimentation and reflection on the application of data science tools elaborated within the frameworks of Rough Set Theory (RST), Formal Context Analysis (FCA) and, though only theoretically, Distributed Information Logic (DIL).
The semiotic objects (s-objects) of this theory can be described in tabular datasets. Nevertheless, at this stage of formalisation of the theory, lattices (not trees) can be used as working representation structures for characterizing the components of concept systems and graphs for categories of each layer.
</abstract>

Compared to existing classical approaches to semiotics which are dyadic (signifier/signified, F. de Saussure) and triadic (symbol/concept/object, Ch. S. Peirce), this theory can be characterized as tetradic ([sign/semion]//[object/noema]) and is the result of either doubling the dyadic approach along the semiotic/ordinary dimension or splitting the ‘concept’ of the triadic one into two (semiotic/ordinary). Other important features of this approach are (a) the distinction made between concepts (only functional pairs of extent and intent) and categories (as representations of expressions) and (b) the indication of the need for providing the mathematical passage from the duality between two sets (where one is a singleton) within systems of sets to category-theoretical monoids within systems of categories while waiting for the solution of this problem in the field of logic.
Last but not least, human language expressions are the most representative physical instances of semiotic objects. Moreover, as computational experiments which are possible with linguistic objects present a high degree of systematicity (of oppositions), in general, it is relatively easy to elucidate their dependence on the concepts underlying signs. This new semiotic theory or rather this new research program emerged as the fruit of experimentation and reflection on the application of data science tools elaborated within the frameworks of Rough Set Theory (RST), Formal Context Analysis (FCA) and, though only theoretically, Distributed Information Logic (DIL).
The semiotic objects (s-objects) of this theory can be described in tabular datasets. Nevertheless, at this stage of formalisation of the theory, lattices (not trees) can be used as working representation structures for characterizing the components of concept systems and graphs for categories of each layer.

Concepts and Categories: A Data Science Approach to Semiotics

Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Concepts and Categories: A Data Science Approach to Semiotics"}

Compared to existing classical approaches to semiotics which are dyadic (signifier/signified, F. de Saussure) and triadic (symbol/concept/object, Ch. S....

Concepts and Categories: A Data Science Approach to Semiotics

Online veröffentlicht: 30. Dez. 2022

Seitenbereich: 169 - 200

DOI: https://doi.org/10.2478/slgr-2022-0010

Schlüsselwörter
Sign theory, human language, data science, formal concept, monoid category, theory of semiotics

© 2022 André Włodarczyk, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Concepts and Categories: A Data Science Approach to Semiotics

Online veröffentlicht: 30. Dez. 2022

Seitenbereich: 169 - 200

DOI: https://doi.org/10.2478/slgr-2022-0010

SchlüsselwörterSign theory, human language, data science, formal concept, monoid category, theory of semiotics

© 2022 André Włodarczyk, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Sign theory, human language, data science, formal concept, monoid category, theory of semiotics