CAREER: An Integrated and Utility-Centric Framework for Federated Text Search

职业：联合文本搜索的集成且以实用程序为中心的框架

• 批准号: 0746830
• 负责人: Luo Si
• 金额: $ 48.1万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746830&HistoricalAwards=false
• 关键词: CAREER; Integrated; Utility; Centric; Framework

Traditional search engines like Google typically ignore a large amount of information behind the search engines of many online text information sources. Federated text search provides one-stop access to the hidden information via a single interface that connects to multiple search engines of text information sources. Existing federated search solutions only focus on content relevance and ignore a large amount of valuable information about users and information sources. This project includes novel research on: (1) Multiple Type Resource Representation: model important information of text information sources such as search response time and search engine effectiveness; (2) Utility-Centric Resource Selection: satisfy a user's search criteria by considering multiple types of evidence such as content relevance, search results from past queries, personal information needs, and search response time; (3) Effective and Efficient Results Merging: produce accurate merged ranked results with little cost of acquiring the content information of the returned documents; (4) System Adaptation by Results Analysis: analyze the search results from past queries for more accurate federated search solutions; (5) System Development and Evaluation: build and test algorithms within research environments as well as a new FedLemur system for a real world application. The project advances the state-of-the-art of research in federated search. It will have broad impacts for other applications such as peer to peer search. The project Web site (http://www.cs.purdue.edu/~lsi/Federated_Search_Career_Award.html) will be used for results dissemination.The education component of the project will expand information retrieval instruction to address multi-disciplinary requirements, improve the education of information technology workforce, and arouse interests of K-12 students for search technologies.

像Google这样的传统搜索引擎通常会忽略许多在线文本信息源的搜索引擎背后的大量信息。联合文本搜索通过连接到多个文本信息源的搜索引擎的单个接口提供对隐藏信息的一站式访问。现有的联合搜索解决方案只关注内容相关性，而忽略了大量关于用户和信息源的有价值的信息。该项目包括以下方面的新研究：（1）多类型资源表示：对文本信息源的重要信息（如搜索响应时间和搜索引擎有效性）进行建模;（2）以实用性为中心的资源选择：通过考虑多种类型的证据（如内容相关性、过去查询的搜索结果、个人信息需求和搜索响应时间）来满足用户的搜索标准;（3）有效和高效的结果合并：产生准确的合并排名结果，而获取返回文档的内容信息的成本很小;（4）通过结果分析的系统适配：分析来自过去查询的搜索结果，以获得更准确的联合搜索解决方案;（5）系统开发和评估：在研究环境中构建和测试算法，以及用于真实的应用程序的新FedLemur系统。该项目推进了联邦搜索的最新研究。它将对其他应用程序（如对等搜索）产生广泛的影响。该项目的网址（http：//www.cs.purdue.edu/callsi/Federated_Search_Career_Award.html）将用于传播成果，该项目的教育部分将扩大信息检索教学，以满足多学科的要求，改善信息技术工作人员的教育，并引起K-12学生对搜索技术的兴趣。