Semantic Dimensionality and Effective Data Fusion in Information Retrieval

信息检索中的语义维度和有效数据融合

• 批准号: 9812086
• 负责人: Paul Kantor
• 金额: $ 9万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9812086&HistoricalAwards=false
• 关键词: Semantic; Dimensionality; Effective; Data; Fusion

This project studies the benefits of combining two or more information retrieval schemes (data fusion). Methods are developed and tested using a large established test collection from the Text Retrieval conferences. The project is developing methods to predict when data fusion will be beneficial, and is developing methods that use non-linear rules of combination. The goal is to achieve a 15% relative improvement in the accuracy of predictions, and a 15% relative improvement in the effectiveness of the fused method. This project is motivated by the explosion of information in the World Wide Web, and includes development of specialized tests for comparing the new methods developed in this project to other "meta-search engines" already on the Web. For further information consult the Web site: http://www.scils.rutgers.edu/research/alexandria/SD.html

该项目研究合并两个或更多信息检索方案(数据融合)的好处。方法是使用来自文本检索会议的大量已建立的测试集合来开发和测试的。该项目正在开发预测数据融合何时有益的方法，并正在开发使用非线性组合规则的方法。目标是实现预测准确度相对提高15%，融合方法的有效性相对提高15%。这个项目的动机是万维网上的信息爆炸，并包括开发专门的测试，将该项目中开发的新方法与Web上已有的其他“元搜索引擎”进行比较。欲了解更多信息，请访问网站：http://www.scils.rutgers.edu/research/alexandria/SD.html。