网络化知识的内涵解析与表征模型构建

互联网时代三元空间的出现改变了知识的本质,带来了全新的知识观:那些在互联网中由群体智慧汇聚的碎片化、展现各自观点、基于问题情境、具有学科交叉的内容成为知识。虽然目前联通主义知识观、回归论知识观已经对这类知识进行描述和阐释,但关于这类知识究竟是什么,又是如何汇聚和发展,并没有术语性的界定,也缺少规律上的认识。本文将这类知识界定为网络化知识,并系统阐述其内涵与特征,同时采用理论建模的方法构建网络化知识表征模型,以期为后续知识生产传播、知识发展演化等基础规律的探究提供有力的抓手。本研究首先基于联通主义知识观和回归论知识观梳理网络化知识的内涵与特征,认为网络化知识是指由群体智慧汇聚生成的信息、理解、技能、价值观和态度,具有动态、个性、群聚、生长以及存在于网络中等基本特征。在此基础上,研究结合网络化知识的新特征,从实体与属性的角度构建了知识表征理论模型,用以表征网络化知识。

火灾致因数据的模糊聚类分析

利用一种成熟的的模糊聚类分析方法,对火灾危险源的前期数据进行分析和处理,为火灾危险源灾变的知识表征模型提供数据准备,得到火灾危险源识别的重要信息。

A New Method of Semantic Network Knowledge Representation Based on Extended Petri Net

Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.