数据与知识协同驱动的知识发现:概念、机理与模型  

Knowledge-Discovery Method Driven by the Collaboration of Data and Knowledge:Concept,Mechanism,and Model

作　　者：姚苏梅 陆泉[1,2,3] Yao Sumei;and Lu Quan(Center for Studies of Information Resources,Wuhan University,Wuhan 430072;School of Information Management,Wuhan University,Wuhan 430072;Big Data Institute,Wuhan University,Wuhan 430072)

机构地区：[1]武汉大学信息资源研究中心,武汉430072 [2]武汉大学信息管理学院,武汉430072 [3]武汉大学大数据研究院,武汉430072

出　　处：《情报学报》2025年第3期282-295,共14页Journal of the China Society for Scientific and Technical Information

基　　金：国家社会科学基金重点项目“心理账户理论视角下在线健康社区精准信息服务研究”(20ATQ008)。

摘　　要：知识发现是应对海量数据和复杂问题挑战,促进科学研究和技术进步,并提高决策支持能力的重要情报理论。“数据”和“知识”是图书馆学、情报学和档案学的核心命题,数据驱动或知识驱动的知识发现是数据密集型或知识密集型情境下解决情报学研究问题的重要手段,但普遍存在的缺陷数据和不确定性知识降低了上述方法的有效性。协同驱动则通过数据和知识的交叉互补,为实现新知识发现提供了创新性的解决途径。当前,对于协同驱动方法的全面和深入分析尚显不足。本文主要目的是按照“是什么”“为什么”和“怎么做”的认知逻辑,梳理数据与知识协同驱动知识发现的基本概念、机理和模型3个方面。首先,提出数据与知识协同驱动知识发现的基本概念,剖析作为该概念重要组成部分的缺陷数据和不确定性知识新概念的含义。其次,机理部分探讨了数据融入知识驱动知识发现和知识融入数据驱动知识发现双视角下协同驱动的多途径和多目的,从数据与知识的交叉互补解释协同驱动知识发现功能实现的本质原因与运作机理。最后,提出问题和场景导向的数据与知识协同驱动知识发现基本模型,并从知识驱动为主(构建模式、纠错模式)、数据驱动为主(嵌入模式、纠正模式和引导模式)和其他协同驱动知识发现(混合模式和并发模式)3类重点阐述了协同驱动内部建模的典型模式。数据与知识协同驱动的知识发现和多种协同驱动的模式兼顾了数据与知识的相互补充和协同作用,为知识发现提供了更全面的框架和流程,为信息资源管理学科拓展了方法创新与问题解决思路。Knowledge discovery is a critical theoretical framework for addressing the challenges posed by vast amounts of data and complex problems,advancing scientific research and enhancing decision support capabilities.“Data”and“knowledge”are core concepts in information science,and knowledge discovery driven by data or knowledge serves as an essential approach to solving research problems in data-intensive or knowledge-intensive contexts.However,pervasive issues of imperfect data and uncertain knowledge limit the effectiveness of these methods.The co-driven approach offers an innovative pathway for discovering new knowledge through the complementary integration of data and knowledge.Despite its potential,a comprehensive and in-depth analysis of co-driven methods remains insufficient.This study adopts a cognitive logic structure of“what,”“why,”and“how”to explore the basic concepts,mechanisms,and models of knowledge discovery driven by the collaboration of data and knowledge.First,it introduces the fundamental concept of knowledge discovery through data-knowledge co-driven mechanisms,along with a detailed explanation of the newly introduced concepts of imperfect data and uncertain knowledge,which are essential components of this framework.Subsequently,the mechanism section examines the multi-path and multi-objective strategies for integrating data into knowledge-driven knowledge discovery and integrating knowledge into data-driven knowledge discovery.It explains the essence and operational mechanisms of co-driven knowledge discovery by emphasizing the cross-complementarity between data and knowledge.Finally,this study proposes a problem-and scenario-driven basic model of knowledge discovery that is co-driven by data and knowledge.It elaborates on three primary categories of internal modeling for co-driven knowledge discovery:predominantly knowledge-driven discovery(construction and error-correction modes),predominantly data-driven discovery(embedding,correction,and guidance modes),and other collaborati

关 键 词：数据与知识协同驱动 缺陷数据 不确定知识 知识发现 协同机理 协同模式 

分 类 号：G250.2[文化科学—图书馆学] G250.7