基于知识挖掘与协同融合的情报研究方法  

Intelligence Research Method Based on Knowledge Mining and Collaborative Fusion

作　　者：肖洪[1] 赵洪[2] 毋晓霞 

机构地区：[1]同方知网(北京)技术有限公司,北京100191 [2]南开大学商学院信息资源管理系,天津300071

出　　处：《情报理论与实践》2018年第10期15-19,共5页Information Studies:Theory & Application

摘　　要：[目的/意义]在当前大数据环境和新型智库建设背景下,传统的情报研究模式难以满足多源知识的挖掘与有效融合,开启以协同为机制激活专家群体智慧的情报研究模式日益迫切。[方法/过程]文章基于"事实数据(显性知识)+专家智慧(隐性知识)"的协同研究机制,以提升情报研究的效率和质量为目标,提出了一种新的情报研究方法。[结果/结论]该方法有效综合大数据挖掘和专家群体智慧优势,有利于提升情报研究水平。[局限]用机器自动总结和提炼专家群体智慧显性化后的知识(自然语言形式),还需要进一步研究。[Purpose/significance] In the context of the big data environment and the new think tank construction,the traditional intelligence research modes cannot satisfy the mining and effective fusion of multi-source knowledge,so the establishment of the intelligence research mode which activates expert group wisdom by collaborative mechanism is more and more urgent and important. [Method/process]This paper uses the method of collaborative mechanism based on massive factual data（ explicit knowledge）and expert group wisdom（ tacit knowledge）,to improve the efficiency and quality of intelligence research,and proposes a new method of intelligence research. [Result/conclusion]The method proposed combines the advantages of comprehensive big data mining and expert group wisdom,which helps to improve the level of intelligence research. [Limitations] Further studies should be made on the methods of machine automatic summarization and refinement for the explicit knowledge generated by expert group wisdom（ natural language form）.

关 键 词：知识挖掘 情报研究 知识协同 隐性知识 

分 类 号：G350[文化科学—情报学]