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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.

摘要IIS-9817473 Chen，Hsinchun University of Arizona $166，666 - 12 mos.DLI第二阶段：高性能数字图书馆分类系统：从信息检索到知识管理这是一个为期三年的连续奖项的第一年资助。拟议的研究旨在开发一个架构和相关技术，需要自动生成分类系统，从大型特定领域的文本集合，并将其与手动创建的分类系统，以协助有效的数字图书馆检索和分析。本项目将在几个样本领域进行算法开发和用户评估。可扩展的自动聚类方法，包括沃德的聚类，多维缩放，潜在的语义索引，和自组织映射将开发和比较。这些算法中的大多数都是计算密集型的，将根据文本文档表示中常见关键字的稀疏性进行优化。使用并行，高性能的平台作为模拟的时间机器，我们计划并行和基准以上聚类算法的大规模集合（数百万文档的顺序）在几个领域。这些自动分类系统的结果将使用几种新颖的层次显示方法来表示。研究的试验床将包括三个应用领域，包括大规模集合和现有的分类系统：（1）医学：CancerLit（700，000癌症摘要）和NLM的UMLS（50万个医学概念），（2）地球科学：GeoRef和石油文摘（800，000摘要）和Georef词库（26，000地球科学术语），和（3）网络应用程序：WWW集合（1.5M网页）和Yahoo！分类（20，000个类别）。评估计划中将使用医学受试者、地球科学家和万维网搜索引擎用户。