Search (2 results, page 1 of 1)

  • × author_ss:"Lin, J."
  • × type_ss:"a"
  1. Lin, J.; DiCuccio, M.; Grigoryan, V.; Wilbur, W.J.: Navigating information spaces : a case study of related article search in PubMed (2008) 0.01 
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    Abstract
    The concept of an "information space" provides a powerful metaphor for guiding the design of interactive retrieval systems. We present a case study of related article search, a browsing tool designed to help users navigate the information space defined by results of the PubMed® search engine. This feature leverages content-similarity links that tie MEDLINE® citations together in a vast document network. We examine the effectiveness of related article search from two perspectives: a topological analysis of networks generated from information needs represented in the TREC 2005 genomics track and a query log analysis of real PubMed users. Together, data suggest that related article search is a useful feature and that browsing related articles has become an integral part of how users interact with PubMed.
  2. Yang, C.C.; Lin, J.; Wei, C.-P.: Retaining knowledge for document management : category-tree integration by exploiting category relationships and hierarchical structures (2010) 0.00 
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    Abstract
    The category-tree document-classification structure is widely used by enterprises and information providers to organize, archive, and access documents for effective knowledge management. However, category trees from various sources use different hierarchical structures, which usually make mappings between categories in different category trees difficult. In this work, we propose a category-tree integration technique. We develop a method to learn the relationships between any two categories and develop operations such as mapping, splitting, and insertion for this integration. According to the parent-child relationship of the integrating categories, the developed decision rules use integration operations to integrate categories from the source category tree with those from the master category tree. A unified category tree can accumulate knowledge from multiple resources without forfeiting the knowledge in individual category trees. Experiments have been conducted to measure the performance of the integration operations and the accuracy of the integrated category trees. The proposed category-tree integration technique achieves greater than 80% integration accuracy, and the insert operation is the most frequently utilized, followed by map and split. The insert operation achieves 77% of F1 while the map and split operations achieves 86% and 29% of F1, respectively.

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