发表机构
Peter the Great St. Petersburg Polytechnic University（圣彼得堡彼得大帝理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将语言作为独立信息层，融合企业词汇概率向量空间与本体论建模，以高效提取知识、弥合统计与语义方法，并识别业务逻辑瓶颈。
AI 中文摘要
基于对语言在思维与沟通中作用的分析，本文提出了一种用于设计企业知识库的新概念。该概念将企业词汇的概率向量空间（反映行业特性、主题焦点、术语和文化）与传统本体论建模相结合。这种结合能够从积累的企业文档中高效提取知识，同时严格考虑特定业务流程。因此，这一概念弥合了统计方法与语义（因果）方法论之间的鸿沟。此外，分析概率空间的投影和因果关系有助于识别业务逻辑中的瓶颈。作为一个动态系统，语言在整体信息架构中充当一个独立的、分离的层。将动态组件引入词分布的概率空间，使其能够被建模为各种问题表述的多维解空间。在此背景下，定义问题条件的输入数据充当动态变换的控制参数。
英文摘要
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.
Comments11 pages, 1 figure. Accepted for publication in the proceedigns of ISBM 2026 (Springer LNNS)
