基于多智能主体协作的知识检索

文章介绍了知识检索、主体及智能主体的涵叉，阐述了知识检索中智能主体的协作策略，并分析了基于多智能主体协作的知识检索模型与方法。     

透视情报学的技术维度研究

本文对情报学的技术维度研究进行了透视，阐述了情报学的技术传统；指出随着情报学研究的向前发展，情报学界对情报学的技术传统予以了反思，对情报学的技术研究进行了重新认识；最后阐述了跨入新世纪的情报学的技术研究新进展。     

复合图书馆的含义、原型及对我们的启示

复合图书馆强调对印刷型资源和电子资源实行整合，为用户提供集成化的检索服务，通过统一界面一次无缝地检索各种资源。目前，这种复合图书馆原型已经出现，为我们探索网络环境下的文献信息服务和数字图书馆建设提供了有益的借鉴。     

从CNKI看全文检索系统的发展

通过回顾CNKI的发展历程。总结其发展经验。推导全文数据库检索系统的发展方向。     

基于WEB的信息检索多媒体CAI课件的优化设计

本文介绍了基于Web的信息检索多媒体CAI课件的实质，在分析信息检索多媒体CAI课件利弊的基础上，探讨了基于Web的信息检索多媒体CAI课件优化设计应遵循的原则及开发的一般策略。     

信息检索可视化初探

本文讨论了信息检索可视化的系统环境、体系结构和人机接口的设计方法。由于信息资源的类型(图、文、声)不同，其检索方法各具特色；其系统内由并列的模块支撑，而外部用统一的界面提供。     

New Media/Internet Research Topics of the Association of Internet Researchers

This study summarizes prior reviews of new media and Internet research, and the growth of the term Internet in academic publications and online newsgroups. It then uses semantic network analysis to summarize the interests and concepts of an interdisciplinary group of Internet researchers, as represented by session titles and paper titles and abstracts from the 2003 and 2004 Association of Internet Researchers conferences. In both years, the most frequent words appearing in the paper abstracts included Internet, online, community, social, technology, and research. The 2003 papers emphasized topics such as the social analysis/research of online/Internet communication, community, and information, with particular coverage of access, individuals, groups, digital media, culture; role and process in e-organizations; and world development. The 2004 papers emphasized topics such as access; news and social issues; the role of individuals in communities; user-based studies; usage data; and blogs, women, and search policy, among others.     

OpenURL框架结构分析

OpenURL作为一种开放式链接框架结构，与传统信息链接框架相比，增加了许多新的功能，成为信息资源整合的重要工具，受到图书情报界的密切关注，也将被美国全国信息标准组织(NISO)所接收。本文在分析OpenURL框架结构和运行机制的基础上，讨论其在信息资源整合中的使用。     

用户认知观视野中的情报检索

回溯了情报检索的历程及研究的重点线路，分析了传统的情报检索及情报检索系统存在的一些难题。针对这些问题，指出了应从用户认知观角度来探索情报检索研究，说明了用户认知观的研究对情报检索理论带来的影响，并从用户认知观的角度对情报检索系统的开发提出了一些设想。     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.