Logo image
Retaining Knowledge for Document Management: Category-Tree Integration by Exploiting Category Relationships and Hierarchical Structures
Journal article

Retaining Knowledge for Document Management: Category-Tree Integration by Exploiting Category Relationships and Hierarchical Structures

Christopher C. Yang, Jianfeng Lin and Chih-Ping Wei
Journal of the American Society for Information Science and Technology, v 61(7), pp 1313-1331
01 Jul 2010

Abstract

Computer Science Computer Science, Information Systems Information Science & Library Science Science & Technology Technology
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 F-1 while the map and split operations achieves 86% and 29% of F-1, respectively.

Metrics

5 Record Views
4 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Information Systems
Information Science & Library Science
Logo image