Conceptualization of knowledge: ideation, design, and realization
Digital Innovation is fundamentally a knowledge-driven effort. Explicit knowledge takes many forms of representation, with different degrees of structure, refinement or operationalization. Over time, knowledge has been captured as natural language text, visual representations, machine-interpretable ontologies, formal rules and logics - each requiring specific management systems and frameworks, built on diverse kinds of knowledge repositories. In the age of Artificial Intelligence, knowledge exchanges and streamlining take place not only between human knowing subjects, but also during human-AI collaborations or directly between AI agents. This requires a revisitation of traditional knowledge management paradigms, considering the new types of interactions and knowledge conversions manifesting in organizations that employ hybrid workforce and value creation teams. Innovation management methods must leverage these new paradigms, as they take an amorphous innovation idea and must gradually refine it through different stages of conceptualization - from early-stage ideation to structured blueprints, simulateable virtualizations and actual realization. Knowledge representation must be able to support all these stages and their progression, and this requires us to investigate the conceptualizations required at each stage and their streamlining possibilities. The talk will reflect on the requirements and expectations arising from this, while giving suggestions and examples on the potential of interplay between enterprise modelling, knowledge graphs and large language models.
Lecture at NEMO2026
Date/Time: Tuesday, July 28, 2026 at 08:30