Articles Information
International Journal of Education and Information Technology, Vol.1, No.2, Jun. 2015, Pub. Date: Jun. 2, 2015
Ontology Similarity Measuring and Ontology Mapping Algorithms Based on Majorization-Minimization Method
Pages: 48-54 Views: 4235 Downloads: 1134
Authors
[01]
Yun Gao, Department of Editorial, Yunnan Normal University, Kunming, China.
[02]
Wei Gao, School of Information Science and Technology, Yunnan Normal University, Kunming, China.
Abstract
Ontology similarity calculation is important research topics in information retrieval and widely used in science and engineering. In information retrieval, ontology is aiming to find the highly semantic similarity information of the original query concept, and then return the results to the user. Ontology mapping is aiming toconstruct the relationship between two or more ontologies. The core trick of ontology applications is to calculate the similarity between the vertices in the ontology graph. By analyzing the technology of majorization-minimization, we propose the new algorithm for ontology similarity measure and ontology mapping. Via the ontology sparse vector learning, the ontology graph is mapped into a line consists of real numbers. The similarity between two concepts then can be measured by comparing the difference between their corresponding real numbers. The experiment results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping in biology and physics applications.
Keywords
Ontology, Similarity Measure, Ontology Mapping, Sparse Vector, Majorization-Minimization
References
[01]
J. M. Przydzial,B.Bhhatarai, and A. Koleti, GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework. Bioinformatics, 29(24)(2013) 3211-3219.
[02]
S.Koehler, S. C.Doelken, and C. J. Mungall, The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Research, 42(D1)(2014)966-974.
[03]
M.Ivanovicand Z.Budimac, An overview of ontologies and data resources in medical domains. Expert Systerms and Applications, 41(11)(2014) 5158-15166.
[04]
A.Hristoskova, V.Sakkalis, andG.Zacharioudakis,Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert. Sensors, 14(1)(2014)1598-1628.
[05]
M. A.Kabir, J.Han, and J.Yu, User-centric social context information management: an ontology-based approach and platform. Personal and Ubiquitous Computing, 18(3)(2014) 1061-1083.
[06]
Y. L.Ma, L.Liu,K.Lu, B. H.Jin, andX. J.Liu, A graph derivation based approach for measuring and comparing structural semantics of ontologies. IEEE Transactions on Knowledge and Data Engineering, 26(3)(2014) 1039-1052.
[07]
Z.Li, H. S.Guo, Y. S.Yuan, andL. B. Sun, Ontology representation of online shopping customers knowledge in enterprise information. Applied Mechanics and Materials, 483(2014) 603-606.
[08]
R.Santodomingo, S., Rohjans, M.Uslar, J. A.Rodriguez-Mondejar, andM.A.Sanz-Bobi,Ontology matching system for future energy smart grids. Engineering Applications of Artificial Intelligence, 32(2014) 242-257.
[09]
T.Pizzuti,G.Mirabelli,M. A.Sanz-Bobi, and F.Gomez-Gonzalez,Food Track & Trace ontology for helping the food traceability control. Journal of Food Engineering, 120(1)(2014) 17-30.
[10]
N.Lasierra, A.Alesanco, and J.Garcia,Designing an architecture for monitoring patients at home: Ontologies and web services for clinical and technical management integration. IEEE Journal of Biomedical and Health Informatics, 18(3)(2014)896-906.
[11]
Y. Y. Wang, W. Gao, Y. G. Zhang, andY. Gao, Ontology similarity computation use ranking learning Method.The 3rd International Conference on Computational Intelligence and Industrial Application, Wuhan, China, 2010, pp. 20-22.
[12]
X. Huang,T. W. Xu, W.Gao, andZ. Y. Jia, Ontology similarity measure and ontology mapping via fast ranking method. International Journal of Applied Physics and Mathematics, 1(2011) 54-59.
[13]
W. Gao, andL. Liang,Ontology similarity measure by optimizing NDCG measure and application in physics education. Future Communication, Computing, Control and Management, 142(2011) 415-421.
[14]
Y. Gao,and W. Gao, Ontology similarity measure and ontology mapping via learning optimization similarity function. International Journal of Machine Learning and Computing, 2(2)(2012) 107-112.
[15]
X. Huang,T.W. Xu,W.Gao, andS. Gong,Ontology similarity measure and ontology mapping using half transductive ranking. In Proceedings of 2011 4th IEEE international conference on computer science and Information technology, Chengdu, China, 2011, pp. 571-574.
[16]
W. Gao, Y.Gao, andL. Liang, Diffusion and harmonic analysis on hypergraph and application in ontology similarity measure and ontology mapping.Journal of Chemical and Pharmaceutical Research, 5(9)(2013) 592-598.
[17]
W. Gao and L. Shi, Ontology similarity measure algorithm with operational cost and application in biology science.BioTechnology: An Indian Journal, 8(11)(2013) 1572-1577.
[18]
W. GaoandT. W. Xu, Ontology similarity measuring and ontology mapping algorithm based on MEE criterion. Energy Education Science and Technology Part A: Energy Science and Research, 32(3)(2014) 3793-3806
[19]
W. Gao,Y. Gao, andY. G. Zhang, Strong and weak stability of k-partite ranking algorithm. Information, 15(11A)(2012)4585-4590.
[20]
W. GaoandT. W. Xu, Stability analysis of learning algorithms for ontology similarity computation. Abstract and Applied Analysis, 2013, 9 pages, http://dx.doi.org/10.1155/2013/174802.
[21]
W. Gao and L.L. Zhu, Gradient learning algorithms for ontology computing. Computational Intelligence and Neuroscience, 2014, 12 pages, http://dx.doi.org/10.1155/2014/438291.
[22]
W. Gao, L. Yan, and L. Liang, Piecewise function approximationand vertex partitioning schemes for multi-dividing ontology algorithm in AUC criterion setting (I).International Journal of Computer Applications in Technology, 50 (3/4)(2014) 226-231.
[23]
N. Craswell andD.Hawking, Overview of the TREC 2003 web track. Proceeding of the Twelfth Text Retrieval Conference, Gaithersburg, Maryland, NIST Special Publication, 2003, pp. 78-92.