摘要
【目的】解决传统数字文献资源内容服务推荐中无法充分挖掘资源语义信息等问题。【方法】通过设定本体推理规则对用户查询关键词进行语义扩展,提出一种新的语义相似度计算方法计算文献资源内容相似度。按照相似度大小对搜索结果进行排序,将排名较高的文献推荐给目标用户。【结果】实验结果证明,该方法能够较准确地计算语义相似度,并能够对用户需求进行有效推荐。【局限】缺少对数字资源的大规模采集,实验案例较少。【结论】该方法充分挖掘数字文献资源的语义信息并进行有效推荐,为数字资源内容服务推荐提供一种新思路。
[Objective] This paper tries to improve the traditional content recommendation service of digital literature, which cannot fully exploit the semantic information of the literature. [Methods] First, we introduced the Ontology reasoning rules to the recommendation system, and then semantically extended the user＇s query. Second, we calculated the similarity of the literature to rank. Finally, we recommend those top ranked literature to the users. [Results] The proposed algorithm can calculate the semantic similarity among literature and successful recommend documents to the users. [Limitations] Only examined the new method with relatively small data sets. [Conclusions] The proposed algorithm could effectively exploit the semantic information digital resource to the users. of target literature and offer a new way to recommend
作者
刘健
毕强
刘庆旭
王福
Liu Jian Bi Qiang Liu Qingxu Wang Fu(School of Management, Jilin University, Changchun 130022, China Inner Mongolia University of Technology Library, Huhhot 010051, China)
出处
《现代图书情报技术》
CSSCI
2016年第9期70-77,共8页
New Technology of Library and Information Service
基金
国家自然科学基金项目"语义网络环境下数字图书馆资源多维度聚合与可视化展示研究"(项目编号:71273111)
"吉林大学高峰学科(群)建设项目"的研究成果之一
关键词
数字文献资源内容
服务推荐
本体推理
语义相似度
Digital literature
Service recommendation
Ontology reasoning
Semantic similarity
