TY - GEN
T1 - A graph structure-based asset retrieval system
AU - Koh, Jia Ling
AU - Chiang, Chia Jung
AU - Chu, Seng Chih
AU - Huang, Yi Chi
AU - Peng, Shao Chun
AU - Wang, Sz Han
AU - Liu, Te Yu
AU - Hsiao, Hui I.
AU - Lin, Chien
AU - Chen, Arbee L.P.
N1 - Publisher Copyright:
© 2015 The authors and IOS Press. All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - asset database application
KW - graph search
KW - information retrieval
UR - http://www.scopus.com/inward/record.url?scp=84926436108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926436108&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-484-8-511
DO - 10.3233/978-1-61499-484-8-511
M3 - Conference contribution
AN - SCOPUS:84926436108
T3 - Frontiers in Artificial Intelligence and Applications
SP - 511
EP - 520
BT - Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
A2 - Chu, William Cheng-Chung
A2 - Yang, Stephen Jenn-Hwa
A2 - Chao, Han-Chieh
PB - IOS Press
T2 - International Computer Symposium, ICS 2014
Y2 - 12 December 2014 through 14 December 2014
ER -