From Symbol to Meaning: Ontological and Philosophical Reflections on Large Language Models in Information Systems Engineering

2026-03-18

AI Summary

This study addresses how large language models (LLMs) challenge traditional ontological, epistemological, and semiotic assumptions about information, representation, and knowledge in information systems engineering. By integrating Peircean semiotics, Heideggerian ontology, and Floridi’s philosophy of information through philosophical analysis and cross-theoretical synthesis, the paper reconceptualizes LLMs as cognitive agents endowed with a form of agentic capacity. It elucidates how LLMs reconfigure the relationships among language, meaning, and system design, and on this basis proposes a novel framework for designing information systems that prioritize human-centered knowledge processes, ethical coherence, and transparency. This work offers an original pathway for reconstructing the philosophical foundations of information systems in the era of LLMs.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The advent of Large Language Models (LLMs) represents a turning point in the theoretical foundations of Information Systems Engineering. Beyond their technical significance, LLMs challenge the ontological, epistemological, and semiotic assumptions that have long structured our understanding of in-formation, representation, and knowledge. This article proposes an integrative reflection on how LLMs reconfigure the relationships among language, meaning, and system design, suggesting that their emergence demands a re-examination of the conceptual foundations of contemporary information systems. Sketching on philosophical traditions from Peirce to Heidegger and Floridi, we investigate how the logic of generative models both extends and destabilises classical notions of ontology and signification. The discussion emphasises the necessity of grounding LLM-based systems in transparent, ethically coherent frameworks that respect the integrity of human-centred knowledge processes. Ultimately, the paper argues that LLMs should be understood not merely as tools for automation but as epistemic agents that reshape the philosophical and semiotic foundations of information systems engineering.

Problem

• Large Language Models
• Ontology
• Semiotics
• Information Systems Engineering
• Epistemology

Innovation

• Large Language Models
• Ontology
• Semiotics
• Epistemic Agents
• Information Systems Engineering