基于信息距离的文献个性化知识发现系统的设计与实现  

Design and Implementation of Personalized Knowledge Discovery System Based on Information Distance

作　　者：刘爱琴[1] 刘扬 Liu Aiqin;Liu Yang

机构地区：[1]山西大学经济与管理学院,山西太原030006

出　　处：《新世纪图书馆》2021年第7期56-62,共7页New Century Library

摘　　要：本文基于信息距离的文献个性化知识发现系统,首先基于文献领域本体对用户输入得到的概念扩展集进行修正处理,形成更符合用户兴趣的概念集合;其次,借助兴趣概念集合,将主题词和与其互相关联的知识匹配,实现用户层级的知识发现;最后,融入基于信息距离和信息层次的个性化推荐算法,对锚定的未评分文献集合进行打分排序,采用Top-N算法,从中挖掘出更深度的知识关联,形成推荐列表,实现个性化的文献知识发现。该系统一方面改善了基于内容的知识发现系统中结果过于专一化和延展性差等问题,扩展了查询粒度;另一方面通过信息量权重的引入,在提高知识检索效率和知识推荐准确度的同时,实现了更为精准的个性化知识发现。Based on the literature personalized knowledge discovery system of information distance,this paper corrects the concept extension set obtained by the user based on the literature domain ontology,and forms a collection of concepts that more in line with the user’s interest.Secondly,with a collection of interest concepts,matching subject words and interrelated knowledge to achieve knowledge discovery at the user level.Finally,the personalized recommendation algorithm based on information distance and information level is integrated to rank the collection of unscored literature,and the Top-N algorithm is used to excavate the deeper knowledge correlation,form the recommendation list and realize the personalized literature knowledge discovery.On the one hand,the system improves the problems of excessive specialization and poor elongation of the results in the content-based knowledge discovery system,expands the granularity of query.On the other hand,with the help of information content,it can realize more accurate personalized knowledge discovery while improving the efficiency of knowledge retrieval and the accuracy of knowledge recommendation.

关 键 词：信息距离 兴趣概念集合 文献个性化 知识发现系统 

分 类 号：G251.6[文化科学—图书馆学]