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

摘要chen，亚利桑那新春大学$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。这是一项为期三年的持续奖励的第一年资助。提出的研究旨在开发一种架构和相关技术，以从大型特定领域的文本集合中自动生成分类系统，并将它们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。在这个项目中，将进行几个样本领域的算法开发和用户评估。对Ward聚类、多维标度、潜在语义索引和自组织映射等可扩展自动聚类方法进行了研究和比较。这些算法中的大多数都是计算密集型的，它们将基于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，我们计划在几个领域中对大规模集合（数以百万计的文档）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的分层显示方法来表示。研究的测试平台将包括三个应用领域，由大规模的收集和现有的分类系统组成：(1)医学：CancerLit（70万癌症摘要）和NLM的UMLS（50万医学概念），(2)地球科学：GeoRef和石油摘要（80万摘要）和GeoRef thesaurus（26000个地球科学术语），以及(3)Web应用：WWW收集（150万个网页）和Yahoo！分类（20000个类别）。评价对象包括医学专业、地球科学家、WWW搜索引擎用户等。

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.