基于领域本体的数字文献资源聚合及服务推荐方法研究

发布时间：2018-06-06 06:57

  本文选题：数字资源聚合 + 语义相似度　； 参考：《情报学报》2017年05期

【摘要】：随着国内外数字资源聚合以及服务推荐研究的不断深入,聚合及服务推荐方法的改进和融合也成为学术研究热点。本文综合运用聚类分析、语义相似度计算、协同过滤推荐算法等方法,提出了基于领域本体的数字文献资源聚合及服务推荐的方法和途径,并以知网文献资源为例,对本文提出的方法流程进行分析研究。结果表明,该方法能够对数字文献资源有效聚合,并挖掘用户需求信息,根据用户偏好对其进行推荐。本文不仅为资源聚合与服务推荐的深入研究搭建了一个新的框架,同时也为资源聚合以服务推荐理论拓展性研究提供了一个新的思路。通过本体、数据挖掘以及服务推荐等方法,对数字图书馆文献资源聚合以及服务推荐进行研究,从而为优化其资源再组织结构和提升知识服务能力提供有价值的参考与指导。
[Abstract]:With the development of digital resource aggregation and service recommendation research at home and abroad, the improvement and integration of aggregation and service recommendation methods has become a hot topic of academic research. Based on cluster analysis, semantic similarity calculation, collaborative filtering and recommendation algorithm, this paper puts forward the methods and approaches of domain ontology based digital literature resource aggregation and service recommendation. The flow of the method proposed in this paper is analyzed and studied. The results show that this method can effectively aggregate the digital document resources and mine the information of users' needs and recommend them according to their preferences. This paper not only provides a new framework for the research of resource aggregation and service recommendation, but also provides a new idea for the research of resource aggregation based on the theory of service recommendation. Through the methods of ontology, data mining and service recommendation, this paper studies the document resource aggregation and service recommendation of digital library, which provides valuable reference and guidance for optimizing the resource reorganizing structure and enhancing the knowledge service ability.
【作者单位】： 吉林大学管理学院;
【基金】：国家自然科学基金，

本文编号：1985677