Chen , Hsinchun University of Arizona $166,666 - 12 mos.
DLI Phase 2: High-Performance Digital Library Classification Systems: From Information Retrieval to Knowledge Management
This 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.
