FAQ知识库的设计与实现

随着网络经济与文化的发展，在大多数商业网站、专业网站、政府及教育行业网站的主页中，几乎都设有FAQ(Frequently Asked Questions)栏目，而且形式多种多样。在图书馆领域，尽管我们拥有分类标引、组织的经验，但还是有许多图书馆的主页中根本就没有这样简单而适用的咨询栏目，更加谈不上在线咨询或虚拟参考咨询了。本文通过对FAQ知识库的建设与设计方法的论述，倡导对FAQ知识库内容的积累，为虚拟咨询知识库做好准备。     

试论公共图书馆参考咨询案例库

本主要通过调查国内二十几个公共图书馆的案例库建设，分析了当前案例库的现状，总结了案例库提供的服务和功能，并且提出了笔对于案例库的一些建议和看法。     

试论图书馆外包开发全文数据库中的版权问题

文章分析了图书馆外包开发全文数据库中存在的版权问题，就在这项工作中有效规避版权风险提出若干措施。     

图书馆Apache服务器日志文件数据的分析

按照标准的服务器日志格式，对图书馆Web服务器日志文件的记录进行分析，并通过对其一定时间段的数据挖掘，对图书馆网站的使用状况进行了有益的探讨。     

基于Agent的网络教育学习信息智能分析研究

提出了一个基于Agent的学习信息智能分析算法，并实现了一个系统原型，该原型利用Agent的特性动态获取学习者的信息，并利用知识库对学习者的信息进行分析，从而实现对用户的自动指导。     

Analysis of Academic Libraries' Facebook Posts: Text and Data Analytics

This research analyzed a dataset of academic libraries' posts on Facebook. It applied a text and data analytics approach to a dataset collected from the Facebook posts of academic libraries at the top 100 English-speaking universities, as listed by the 2014 Shanghai World University Rankings. The dataset is from a two-year posting history of 18,333 unique posts, 113,621 likes, and 3401 comments. Less than a quarter of the libraries had more than 2000 post-related likes, and only seven received more than 100 comments on their postings. Content analysis identified the most prevalent single word (unigrams), bigrams (two-word sequences), and trigrams (three-word sequences) in high and low engagement content. Semantic analysis identified the semantic categories for posts with high and low engagement. The findings can assist academic libraries in their social media strategies for engagement, marketing, and visibility.     

巩固执政基础,提高党的执政能力

党作为中国特色社会主义事业的坚强核心,要巩固自己的执政地位,必须具有扎实的人民基础、雄厚的经济基础、先进的思想文化基础。只有如此,才能不断提高党在社会的影响力和凝聚力,不断增强党的执政能力。     

Assessing library topics using sentiment analysis in R: a discussion and code sample

Abstract

This article discusses the use of R programing language for executing a sentiment analysis of tweets pertaining to library topics. This discussion is situated within the literature of marketing and management sciences, which is employing methods of machine learning and business intelligence to make informed decision-making, and library administration, which has expressed great interest in social media engagement within its literature but has yet to adopt these types of analysis. Presented in this article is a sample code with instructions on how users may execute it within R to retrieve and analyze tweets relevant to library services. Two examples created using the code (analysis of top librarians’ tweets and analysis of posts about major book publishers) are used to demonstrate the functionality of the code. The code presented in this article may be used by libraries to analyze tweets about their library and library-related topics, which, in turn, may inform management and marketing design.     

高校图书馆建设地方特色文献数据库的思考

论述了高校图书馆创建地方特色献数据库的意义、必要性、可行性及及创建方法。