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

像谷歌这样的传统搜索引擎通常忽略了搜索引擎背后的大量信息，这些信息是许多在线文本信息源的搜索引擎。联邦文本搜索通过一个接口提供对隐藏信息的一站式访问，该接口连接到文本信息源的多个搜索引擎。现有的联邦搜索解决方案只关注内容相关性，而忽略了关于用户和信息源的大量有价值的信息。本项目在以下方面进行了新颖的研究：(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、搜索引擎有效性等进行建模；(2)以效用为中心的资源选择：通过考虑内容相关性、过去查询的搜索结果、个人信息需求、搜索响应时间等多种证据来满足用户的搜索标准；(3)有效高效的结果合并：在获取返回文档内容信息的成本很小的情况下，产生准确的合并排序结果；(4)通过结果分析来适应系统：分析过去查询的搜索结果，以获得更准确的联邦搜索解决方案；(5)系统开发和评估：在研究环境中构建和测试算法，以及用于现实世界应用的新的FedLemur系统。该项目推进了联邦搜索中最先进的研究。它将对其他应用产生广泛的影响，比如点对点搜索。项目网站（http://www.cs.purdue.edu/~lsi/Federated_Search_Career_Award.html）将用于发布结果。该项目的教育部分将扩展信息检索指导，以满足多学科要求，改善信息技术劳动力的教育，并引起K-12学生对搜索技术的兴趣。

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.