图书馆自助服务的优势及实施保障

文章阐述了图书馆自助服务的基本内涵及典型特征,分析了其主要优势,提出了图书馆自助服务的实施保障。     

对区级图书馆参考咨询服务发展趋势的思考

参考咨询工作应适应信息时代的需要，本文从建立文献信息资源系统，研究探讨适应区级公共图书馆参考咨询服务的方法和手段。为广大读者提供快捷的服务等方面入手。对区一级图书馆参考咨询服务发展趋势进行了有苴的探讨。参考文献4。     

图书馆、情报和档案学数字信息群的知识集成研究进展与关键问题

图书馆、情报和档案学大学科数字信息群的知识共享是学科各领域协调发展和跨领域知识集成的重要基础。实现学科数字信息群的知识共享集成和挖掘,需要创新研究相关关键技术。目前国际上的研究处于起步阶段。提出我国对数字信息群知识集成研究的方法和基本路向。参考文献13。     

论政府信息资源生命周期管理

政府信息资源从产生到消亡是一个完整的过程,符合生命周期思想的内涵。政府信息资源周期的4个阶段是:产生期、处理期、应用期、衰退期。构建政府信息资源生命周期管理模型,以初步构筑起政府信息资源生命周期理论体系。图1。参考文献10。     

电子政务动态信息采集模型的研究

电子政务动态信息采集模型的设计,要有高效、便利的信息采集平台,支持多种数据格式,提供丰富的检索手段,有支持数字信息的安全管理与审核机制,有开放的多元化的信息资源发布平台,提供资源整合手段。其各项功能由6个子模块提供。图1。参考文献7。     

“信息资源共享”课程的立体化建设

中山大学资讯管理系以纸本《信息资源共享》为核心内容,研制成“信息资源共享网络课程”,采用数字化网络平台进行网络教学,构建了课程的教学立体化体系,教学效果良好。图 4。参考文献7。     

基于协同理论的企业信息系统集成框架

协同理论有两层含义:企业内部资源协同,企业外部资源协同即供应链的协同。以协同理论和信息系统的集成方法为依托,提出制造业企业信息系统集成的框架。框架基于数据挖掘技术和Web Services技术,内部协同框架表现为企业应用集成,外部协同框架表现为协同商务平台。整个框架提高了企业内外部信息传递和沟通的有效性,保证了供应链上各个环节信息的有效收集和传递,实现了企业价值增值。图2。参考文献7。     

Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.     

Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.