摘要
针对如何对企业经验知识进行规范审阅的问题,结合Wiki平台的特征和隐性知识积累的形式,采用Okapi BM2500权重计算算法考虑用户与词条的相关度,运用改进的PageRank算法计算用户-词条关系网中用户的权威度,根据Cascade排序方式得到推荐专家列表,提出了基于隐性知识积累平台——MediaWiki的专家推荐方法.结合中国某大型造船厂的工艺经验知识,通过实验,验证了基于MediaWiki平台的专家推荐方法的有效性.
Taking into account the characteristics of MediaWiki platform and empirical knowledge accumulation mechanism,an expert recommendation mechanism has been proposed which considers the knowledge relevance between users and items and authority among users simultaneously,adopts the Okapi BM 2500 model to calculate users＇ knowledge relevance,refines the PageRank algorithm for better performance of users＇ authority calculation,and obtains the final experts list with Cascade collation.Moreover,a series of comparison experiments have been conducted,which fully verify the efficiency and effectiveness of the proposed expert recommendation mechanism.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2015年第12期1833-1841,共9页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金项目(70971085
71271133)
上海市教育委员会科研创新重点项目(13ZZ012)
上海市科委科技创新行动计划项目(13111104500)资助
关键词
专家推荐
MediaWiki平台
工程经验知识
知识管理
expert recommendation mechanism
MediaWiki platform
engineering empirical knowledge
knowledge management
