Ontology-based knowledge fusion framework

被引：6
|
作者：
Xu C. [1 ,2 ]
Li A. [1 ]
Liu X. [1 ]
机构：
[1] Institute of Advanced Manufacturing Technology, Tongji University
[2] School of Mechanical and Electronic Engineering, Huangshi Institute of Technology
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2010年 / 22卷 / 07期
关键词：
Fusion algorithm; Fusion-knowledge metric; Knowledge evaluation; Knowledge fusion; Meta-knowledge; Ontology;
D O I：
10.3724/SP.J.1089.2010.10897
中图分类号：
学科分类号：
摘要：
An ontology-based knowledge fusion framework is presented to improve semantic specification and accuracy of fusion-knowledge. The framework includes the construction of meta-knowledge sets, calculation of fusion-knowledge metric, knowledge fusion algorithm, and post-processing for fusion-knowledge. Knowledge fusion pattern is analyzed and the construction method for the meta-knowledge set is presented according to the relationship between knowledge elements in domain ontology. Semantic relativity is analyzed using maximum entropy models. Fusion-knowledge metric with relationship strength and weight of knowledge elements is formulated. Genetic simulated annealing fusion algorithm, fusion rules and evaluation mechanism based on information diffusion theory are developed. Finally, the effectiveness of the knowledge fusion framework is demonstrated by an illustrative example. The results show that it is beneficial to control the scale of new knowledge, and improve semantic relativity and accuracy of fusion-knowledge.
引用
下载
收藏
页码：1230 / 1236
页数：6
相关论文
共 17 条
  • [1] Deng Y., Shi W.K., Experts/knowledge fusion in model-based diagnosis based on Bayes networks, Journal of Systems Engineering and Electronics, 14, 2, pp. 25-30, (2003)
  • [2] Preece A., Hui K., Gray A., Et al., KRAFT: an agent architecture for knowledge fusion, International Journal of Cooperative Information Systems, 10, 1-2, pp. 171-195, (2001)
  • [3] Scherl R., Ulery D.L., Technologies for Army Knowledge Fusion, (2004)
  • [4] Kuo T.T., Tseng S.S., Lin Y.T., Ontology-based knowledge fusion framework using graph partitioning, Lecture Notes in Artificial Intelligence, 2718, pp. 11-20, (2003)
  • [5] Martens D., Backer M.D., Haesen R., Et al., Ant-based approach to the knowledge fusion, Lecture Notes in Computer Science, 4150, pp. 84-95, (2006)
  • [6] Laskey K.B., Costa P.C.G., Janssen T., Probabilistic ontologies for knowledge fusion, Proceedings of the 11th International Conference on Information Fusion, pp. 1-8, (2008)
  • [7] Zheng X.Q., Wu Z.H., Chen H.J., Knowledge fusion in semantic grid, Proceedings of the 5th International Conference on Grid and Cooperative Computing, pp. 424-431, (2006)
  • [8] Xie N.F., Cao C.G., Guo H.Y., A knowledge fusion model for web information, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, pp. 67-72, (2005)
  • [9] Gou J., Jiang Y.L., Wu Y.Y., Et al., A new self-adapting knowledge fusion system, Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 454-458, (2007)
  • [10] Gou J., Yang J., Jiang Y., Et al., Knowledge fusion system based on meta-information and ontology, Journal of Computer-Aided Design & Computer Graphics, 18, 6, pp. 819-823, (2006)
12