基于向量空间的知识拓扑结构模型研究

A Knowledge Topologic Structure Model Based on Vector Space

本文以知识拓扑结构为研究对象,针对其在应用中的局限性及运行效率等问题,提出一种基于向量空间的知识拓扑结构模型。该模型将组织中独立初创性知识和派生知识的全体构成一个n维向量空间,并与其伴随向量空间中的向径相对应。从初创性知识到派生知识的过程就转化为n维向量空间内对应向径间的线性组合,对知识的正逆向搜索、知识动态变化等操作通过相应向径间的内积运算来实现。该模型能够清晰地体现知识间的内在联系,在搜索性能等方面得到了相应的改善。

With kownledge topologic structure as the reserch target the paper proposes a novel model based on n-dimensional vector space to solve some limitations in the processes of knowledge applications. All parts of knowledge could construct a vector space, and then each of them has a corresponding vector in the concomitant vector space of the vector space. So the process of knowledge transform were represented by corresponding linear combination, and all operations on knowledge topologic structure can be done by calculating the inner product between corresponding vectors. The model can describe the relationship among knowledge clearly. And the flexibility, searching performances of the new knowledge topologic structure model are modified.