个性化网络学习的语义信息检索研究  

Research on Semantic Information Retrieval for Personalized E-learning

作　　者：俞扬信[1] 

机构地区：[1]淮阴工学院计算机工程学院,淮安223003

出　　处：《情报学报》2012年第1期18-22,共5页Journal of the China Society for Scientific and Technical Information

基　　金：本文系淮阴工学院科研基金项目“基于语义的三维模型检索技术研究”（项目编号：HGB0907）的研究成果之一.

摘　　要：个性化网络学习是现代信息检索技术的新形式。快速高效地获得所需要的信息是每个用户的迫切要求，个性化信息检索技术则是实现高质量信息服务的前提。本文提出了一种网络学习环境下的个性化检索方法，该方法采用Web语义表示学习内容，将用户偏好看作本体，在将包含关键词映射到本体的基础上，进行搜索结果的重排。本方法有一重要特点，就是文档的合并分类实现了文档自身的提取和数据驱动，使用户更容易适应不同的学习平台，更容易演变文档集。实验结果表明，由于网络学习内容检索的准确率和召回率的提高，特别在用户过去活动的基础上进行搜索结果的重排，用户内容可有效地被使用。Personalized e-learning is a new form of modem information retrieval technologies. The user quickly and efficiently accessing the needed information is an urgent requirement for each user, and personalized information retrieval technology is a prerequisite to achieve high-quality information services. The paper presents an approach for personalized retrieval in an e-learning platform, that takes advantage of Web semantic to represent the learning content and the user profiles as ontologies, and that re-ranks search results based on the contained terms map to these ontologies. One important aspect of our approach is the combination of the documents and a taxonomy, with the data driven extraction of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document collection. Our experimental results show that the learner＇s context can be effectively used for improving the precision and recall in e-learning content retrieval, particularly by re-ranking the search results based on the learner＇s past activities.

关 键 词：个性化 信息检索 网络学习 用户偏好 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]