DLI-Phase 2: High-Performance Digital Library Classification Systems: From Information Retrieval to Knowledge Management

DLI-阶段 2：高性能数字图书馆分类系统：从信息检索到知识管理

• 批准号: 9817473
• 负责人: Hsinchun Chen
• 金额: $ 50万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2002-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9817473&HistoricalAwards=false
• 关键词: DLI; Phase; Performance; Digital; Library

AbstractIIS-9817473Chen , Hsinchun University of Arizona$166,666 - 12 mos.DLI Phase 2: High-Performance Digital Library Classification Systems: From Information Retrieval to Knowledge ManagementThis is the first year funding of a three year continuing award. The proposed research aims to develop an architecture and the associated techniques needed to automatically generate classification systems from large domain-specific textual collections and to unify them with manually created classification systems to assist in effective digital library retrieval and analysis. Both algorithmic developments and user evaluation in several sample domains will be conducted in this project. Scalable automatic clustering methods including Ward's clustering, multi-dimensional scaling, latent semantic indexing, and self-organizing map will be developed and compared. Most of these algorithms, which are computationally intensive, will be optimized based on the sparsity of common keywords in textual document representations. Using parallel,high-performance platforms as a time machine for simulation, we plan to parallellize and benchmark the above clustering algorithms for large-scale collections (on the order of millions of documents) in several domains. Results of these automatic classification systems will be represented using several novel hierarchical display methods.The testbed of research will include three application domains that consist of both large-scale collections and existing classification systems: (1) medicine: CancerLit (700,000 cancer abstracts) and the NLM's UMLS (500,000 medical concepts), (2) geoscience: GeoRef and Petroleum Abstracts (800,000 abstracts) and Georef thesaurus (26,000 geoscience terms), and (3) Web application: a WWW collection (1.5M web pages) and the Yahoo! classification (20,000 categories). Medical subjects, geo scientists, and WWW search engine users will be used in the evaluation plan.

摘要IIS-9817473Chen，亚利桑那州新春大学$166,666 - 12 mos.DLI 第 2 阶段：高性能数字图书馆分类系统：从信息检索到知识管理这是三年连续奖的第一年资助。 拟议的研究旨在开发一种架构和相关技术，从大型特定领域文本集合中自动生成分类系统，并将其与手动创建的分类系统统一起来，以协助有效的数字图书馆检索和分析。该项目将在多个示例领域进行算法开发和用户评估。 将开发和比较可扩展的自动聚类方法，包括 Ward 聚类、多维缩放、潜在语义索引和自组织映射。大多数计算密集型算法将根据文本文档表示中常见关键字的稀疏性进行优化。使用并行的高性能平台作为模拟的时间机器，我们计划对多个领域的大规模集合（数百万个文档的量级）的上述聚类算法进行并行化和基准测试。这些自动分类系统的结果将使用几种新颖的分层显示方法来表示。研究的测试平台将包括由大规模馆藏和现有分类系统组成的三个应用领域：（1）医学：CancerLit（700,000 份癌症摘要）和NLM 的 UMLS（500,000 个医学概念），(2) 地球科学：GeoRef 和石油摘要（800,000 个摘要）和 Georef 同义词库（26,000 个地球科学术语），以及 (3) Web 应用程序：WWW 集合（150 万个网页）和 Yahoo!分类（20,000 个类别）。医学受试者、地球科学家和WWW搜索引擎用户将被用于评估计划。