A new concept for designing corporate knowledge bases is proposed that integrates the probabilistic vector space of a corporate vocabulary, reflect-ing industry specifics, subject focus, terminology, and culture, with traditional ontological modeling, and enables the efficient extraction of knowledge from accumulated corporate documents.
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.
No document is ever relevant. The phrase works in practice because one component of a complex relation is made to stand for the whole. Theory cannot afford that compression. Relevance obtains only when some perceived feature of a document becomes consequential for a subject. The document is involved, but relevance...
William D. Senn, M. Buckland· Journal of Documentation· 0 citations
Organizations depend on expert judgement to interpret complex operational situations and maintain consistent decision-making under conditions of uncertainty, regulatory complexity and contextual variation. In SMEs as well as multinational enterprises, the reasoning structures underlying such decisions remain only parti...
Manfred Bornemann· European Conference on Knowl...· 0 citations
As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity. However, most structured knowledge systems remain node-centric, representing files, concepts, results, and judgm...
Yu-Yang Zheng, Nan Li, Wen-Xia Deng et al.· 0 citations
While generative artificial intelligence (generative AI) is profoundly reshaping organizational communication paradigms, it has also triggered a crisis of “technological alienation.” This crisis manifests as information distortion caused by algorithmic hallucinations and the weakening of agency among communication acto...
Ting Qiao, Xin-Qiang Wang, Yi-Zhang Tian et al.· Journal of Organizational an...· 0 citations
Classical information theory, originating with Shannon’s foundational work [1], treats information as a purely syntactic quantity divorced from meaning. Intelligent systems operating in real-world environments must nonetheless process semantic content carried in natural language — content permeated by vagueness, fuzzin...
Jayesh Vijay Rao Karanjgaonkar· International Journal of Mat...· 0 citations
Sociological systems theory offers a rich vocabulary for meaning, observation, and communication, but that vocabulary has proven difficult to translate into executable, testable, and revisable procedures. This gap has become more consequential as large language models are increasingly used to read, summarize, and simul...
Hans Mund· Frontiers in Sociology· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.