Corporate knowledge bases gain new language driven design layer

Language as an Independent Information Layer: A Conceptual Model of Communication, Cognition and Decision-Making

Computation and LanguageComputational Engineering, Finance, and ScienceComputers and SocietyMachine Learning

Summary

Figuring out how language shapes thinking and communication, the authors suggest a new way to build knowledge bases for companies. They combine word meaning patterns with structured business knowledge to find useful information in documents while respecting how a company operates. This approach helps connect statistical word data with deeper cause-and-effect understanding. By examining how word usage and business logic relate, it can reveal where processes might get stuck. Language acts like its own layer of information that changes dynamically depending on the problem being solved.

What this means in practice

A position paper. It proposes an approach and reports no results.

Authors

Anastasiia Alifanova, Elena Benderskaya

Abstract

Based on an analysis of the role of language in thought and communication, this article proposes a new concept for designing corporate knowledge bases. The concept integrates the probabilistic vector space of a corporate vocabulary, reflect-ing industry specifics, subject focus, terminology, and culture, with traditional ontological modeling. This combination enables the efficient extraction of knowledge from accumulated corporate documents while strictly accounting for specific business processes. Consequently, this concept bridges statistical and semantic (cause-and-effect) methodologies. Furthermore, analyzing the projec-tions of probabilistic spaces and causal relationships can help identify bottlenecks in business logic. As a dynamic system, language functions as a separate, inde-pendent layer within the overall information architecture. Introducing a dynamic component into the probabilistic space of word distribution allows it to be mod-eled as a multidimensional solution space for various problem formulations. In this context, input data defining the problem conditions serve as control parame-ters for dynamic transformations.
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