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

基本信息

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

项目摘要

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.

Abstractiis-9817473Chen，亚利桑那州Hsinchun大学$ 166,666-12 MOS.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，这是三年持续奖励的第一年资金。拟议的研究旨在开发一种体系结构和相关的技术，以自动从大型领域特定的文本集合中生成分类系统，并使用手动创建的分类系统统一它们，以帮助有效的数字图书馆检索和分析。该项目将在几个样本域中进行算法开发和用户评估。可扩展的自动聚类方法，包括Ward的聚类，多维缩放，潜在的语义索引和自组织图。这些算法中的大多数是计算密集型的，将根据文本文档表示中常见关键字的稀疏性进行优化。我们计划使用并行的高性能平台作为模拟时间机，我们计划在几个域中对上述大规模集合（按数百万个文档的订单）进行平行和基准测试集群算法。 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地球科学术语）和（3）Web应用程序：www Collection（150万网页）和Yahoo!分类（20,000个类别）。评估计划将使用医学主题，地理科学家和WWW搜索引擎用户。