Martin Thellefsen

Drawing on Knowledge Organization (KO) theory, this paper offers a sociotechnical framework for integrating artificial intelligence (AI) into the production and classification of knowledge. We argue that the dominant AI paradigm, which frames classification as a statistical optimization task, overlooks the situated, social, and historical dimensions that give knowledge meaning. To bridge this gap, we introduce the domain-analytic framework from KO as a methodology for designing more context-aware and epistemically responsible AI systems. The paper proceeds in three parts. First, it identifies the limitations of current AI by contrasting its underlying assumptions with the core principles of domain analysis. Second, it translates these principles into a heuristic framework with actionable design questions, offering a practical path for aligning AI systems with the values and practices of specific knowledge communities. Finally, it explores the more profound philosophical challenges posed by generative AI, using the concept of semiosis to examine how machinic interpretation reshapes our understanding of documents, stability, and meaning itself. By reframing classification as both a technical and an interpretive practice, this paper provides a foundation for developing AI systems that are more reflexive and responsive to the social dynamics of human knowledge.

The paper presents the concept significance-effect outlined in a Peircean inspired communication model, named DynaCom. The significance effect is a communicational effect; the formal conditions for the release of the significance-effect are the following: (1) Communication has to take place within a universeof discourse; (2) Utterer and interpreter must share collateral experience; and (3) The cominterpretant must occur. If these conditions are met the meaning of thecommunicated sign is likely to be correctly interpreted by the interpreter. Here, correctly means in accordance with the intentions of the utterer. The scope of thesignificance-effect has changed from knowledge effects caused by technical terms to emotional effects caused by lifestyle values in brands, for example.

What is the relation between emotion, information, and knowledge? The aim of the paper is to focus on the meaning creation process, which involves emotion, information, and knowledge. The paper shows how the meaning creation process is an intricate relation between information and knowledge, how information is the catalyst for knowledge, and how knowledge can effect information, while emotion is the flavor of information and knowledge, respectively. The paper also presents a concept of information based on Peirce’s semeiotic.