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A graph structure-based asset retrieval system
Conference paper   Peer reviewed

A graph structure-based asset retrieval system

Jia-Ling Koh, Chia-Jung Chiang, Seng-Chih Chu, Yi-Chi Huang, Shao-Chun Peng, Sz-Han Wang, Te-Yu Liu, Hui-I Hsiao, Chien Lin and Arbee L.P. Chen
Frontiers in Artificial Intelligence and Applications, Vol.274, pp.511-520
2015

Abstract

asset database application graph search information retrieval Artificial Intelligence
Retrieving assets for reuse is often a laborious, time-consuming, and difficult task because asset information cannot be effectively maintained. In this study, an asset searching technology was developed by using the graph structures and attributes. (1) The searching strategy based on graph structure primarily considers the structural relationships between assets to evaluate the similarity between asset graphs and the query. (2) The searching strategy based on attributes uses graph structures for fast retrievals, and performs string matching on the asset documents of the graph matching results to determine the degree of similarity between an asset solution and the query according to their content descriptions and attributes. To combine the matching results of both the structures and attributes, this study developed an overall similarity evaluation and ranking mechanism to search and identify the asset solutions that most similar to the query requirement. This study provides a comprehensive asset similarity evaluation method, which can improve the effectiveness of searching assets and usability of asset resources.

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