面向构建过程的范畴学习模型及其适应性机制研究

项目名称： 面向构建过程的范畴学习模型及其适应性机制研究

项目编号： No.61503044

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 赵健

作者单位： 长春工程学院

项目金额： 21万元

中文摘要： 范畴学习在基于人类认知规律的概念建模研究中具有重要意义。目前，概念建模方法对概念的可扩展边界、范畴的构建过程等方面缺乏深入研究，以及由于公理化的建模方式对认知过程形成约束，导致概念模型在变化环境中的适应能力较弱。借鉴原型理论，本项目拟构造“锚概念”作为可扩展的概念结构，并在其基础上建立范畴学习的形式化框架。围绕基本思想“基于概念的知识获取可描述为范畴的构建过程”，本项目针对现存问题，提出：一）范畴的语义内涵具有动态稳定的家族相似性；二）范畴的构建基于锚概念之间相互干扰的过程。拟采用拓扑学方法构造锚概念的可扩展边界，并基于Hilbert空间对锚概念之间的干扰过程进行描述，从而对范畴学习的适应性机制展开研究。本项目为进一步阐明基于经验的范畴学习规律、探索知识获取过程中处理不协调信息的机制提供新的思路；也为建立符合认知规律的概念模型，及其在语义推理、社会网络链接预测等领域中的应用奠定理论基础。

中文关键词： 概念建模；知识表示；不确定性知识获取；范畴学习；锚概念

英文摘要： Category learning is an important way to establish conceptual system model based on cognitive rules of human beings. However, no deep research on the extensible boundaries of concept and category establishment process of existing conceptual modeling methods has been reported yet. Axiomatic modeling methods will restrict the cognitive process, resulting in the poor adaptation of conceptual model in changing environment. Based on prototype theory, this program plans to build an “anchor concept” as the extensible conceptual structure to build the formal framework of category learning. Centering on the basic idea that “knowledge acquisition based on concepts are establishment process of categories” and focusing on existing problems, this program proposed that: 1) Semantic connotation of the category has family resemblance of dynamic stability; 2) category establishment is based on the mutual interference of anchor concepts. To study adaptation mechanism of category learning, this program creates extensible boundaries of anchor concept by using topology method and describes the mutual interference mechanism of anchor concepts based on Hilbert space. Research results could not only provide a new idea for further illustration of experience-based category learning law and exploration of inconsistent information processing mechanism during knowledge processing, but also lay theoretical foundations for establishing a conceptual model conforming to cognitive rules as well as its application in semantic inference and social network linking prediction.

英文关键词： Concept Modeling;Knowledge Representation;Uncertainty Knowledge Acquisition;Category Learning;Anchor Concept