收稿日期: 2015-03-20
修回日期: 2015-04-09
网络出版日期: 2015-04-20
基金资助
本文系中央高校基本科研业务费专项资金资助重点项目"替代计量学理论与实证研究"(项目编号:2014104010201)研究成果之一。
Research Progress and Trends of International Knowledge Fusion at the Perspective of Knowledge Science
Received date: 2015-03-20
Revised date: 2015-04-09
Online published: 2015-04-20
[目的/意义]归纳分析知识科学视角下国际知识融合研究主题和内容,探索其研究趋势,以期为图书情报领域开展知识融合研究,进而促进知识服务和情报研究提供参考和借鉴。[方法/过程]系统梳理Web of Science中1990-2015年的相关文献,通过逐篇阅读、提炼和总结,从实现路径、评价、系统和应用研究4个方面进行主题分析。[结果/结论]知识融合的实现路径方面主要包括基于语义规则、基于贝叶斯网络、基于D-S理论和基于知识挖掘的知识融合;知识融合评价研究方面开发了具备属性值的融合知识测度和自适应评价机制;知识融合系统研究方面,在经典的KRAFT知识融合系统基础上,开发了KnoFuss等知识融合系统;知识融合应用研究方面,主要有面向网格环境、面向近似知识、面向网络文本和面向企业知识的知识融合。未来几年知识融合的研究趋势有:知识体系结构的优化,融合算法的改进,与相关学科的交叉以及结合大数据环境等社会背景和面向多层次、个性化、创新型知识服务方面的研究。
邱均平 , 余厚强 . 知识科学视角下国际知识融合研究进展与趋势[J]. 图书情报工作, 2015 , 59(8) : 126 -132,148 . DOI: 10.13266/j.issn.0252-3116.2015.08.018
[Purpose/significance]The paper summarizes and analyzes the research themes and contents of international knowledge fusion at the perspective of knowledge science, and explores its research trends. Its aim is to provide reference and guidance for LIS discipline to conduct knowledge fusion research and further improve knowledge service and information study.[Method/process]The paper systematically reviews the related literature indexed in WOS from 1990 to 2015, reads, refines and summarizes each paper to conduct topic analysis from four aspects, which are realization approach, evaluation, system and application study.[Result/conclusion]The paper reveals that implementation approaches of knowledge fusion mainly include the ones based on semantic rules, Bayesian networks, D-S theories and knowledge mining; Knowledge fusion evaluation study develops the mechanism of fusion knowledge measures with attributes and self-adapting; Knowledge fusion system study develops the new knowledge fusion systems like KnoFuss based on the classical KRAFT knowledge fusion system; Knowledge fusion application study mainly includes web text oriented, approximate knowledge oriented, company knowledge oriented and web grid environment oriented ones. Trends of knowledge fusion study in the near future are enhancement of architecture, improvement of fusion algorithms, intersection of related disciplines, combination of social background like big data, multi-level, individualized and innovative knowledge service oriented.
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