An ontology based semantic heterogeneity measurement framework for optimization in distributed data mining

Proceedings - International Conference on Machine Learning and Cybernetics

Volumn 1, Issue , 2012, Pages 118-123

  Liu, Bin ^a,b   Cao, Shu Gui ^b   Cao, Dong Fang ^b   Li, Quing Chun ^b   Liu, Hai Tao ^b   Shi, Shao Nan ^c  

^a TSINGHUA UNIVERSITY   (China)

^b HEBEI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c HEBEI UNIVERSITY OF ECONOMICS AND BUSINESS   (China)

Author keywords

distributed data mining; ontology; Semantic heterogeneity

Indexed keywords

DATA-SOURCES; DISTRIBUTED DATA MINING; ELEMENT LEVEL; ONTOLOGY-BASED; POTENTIAL RISKS; SEMANTIC DISTANCE; SEMANTIC HETEROGENEITY; VOTING METHOD;

CYBERNETICS; LEARNING SYSTEMS; ONTOLOGY; OPTIMIZATION; SEMANTICS;

DATA MINING;

EID: 84871662213     PISSN: 2160133X     EISSN: 21601348     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2012.6358897     Document Type: Conference Paper

References (24)

1. Halevy. A. Y. "Why your data won't mix: semantic heterogeneity", ACM Queue, Vol. 3, No. 8, pp. 50-58, 2005.
2. Jung. J. J. "Semantic preprocessing for mining sensor streams from heterogeneous environments", Expert Systems with Applications, Vol. 38, No. 5, pp. 6107-6111, 2011.
3. Egenhofer. M. "Toward the semantic geospatial web", Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, Virginia, USA, pp. 1-4, November. 2002.
4. Lutz. M., Sprado. J., Klien. E., Schuber. C., and I. Christ, "Overcoming semantic heterogeneity in spatial data infrastructure", Computers & Geosciences, Vol. 35, No. 4, pp. 739-752, 2009.
5. Bergamaschi. S., Castano. S., Vincini. M., and D. Beneventano, "Semantic integration of heterogeneous information sources", Data & Knowledge Engineering Vol.36, No. 3, pp. 215-249, 2001.
6. Bin Liu, Shugui Cao, and Wu He, "Distributed data mining for e-business", Information Technology and Management, Vol.12, No. 2, pp. 67-79, 2011.
7. Wirth. R, Borth. M., and J. Hipp, "When distribution is part of the semantics: a new problem class for distributed knowledge discovery", Proceedings of PKDD-2001 Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, Freiburg, Germany, pp. 56-64, September, 2001.
8. Bin Liu, Shugui Cao, Qingchun Li, and Qi Li, "A hierarchical distributed data mining architecture", Proceedings of 2011 International Conference on Machine Learning and Cybernetics, Guilin, China, pp. 40-44, 2011.
9. Gaya. M. C., and J. I. Giráldez, "Merging local patterns using an evolutionary approach", Knowledge and Information Systems, Vol. 29, No. 1, pp. 1-24, 2011.
10. Moretti. C., Bui. H., Hollingsworth. K., Rich. B., Flynn. P., and D. Thain, "All-Pairs: An abstraction for data-intensive computing on campus grids", IEEE Transactions on Parallel and Distributed Systems, Vol. 21, No. 1, pp. 33-46, 2010.
11. Tomás. V. A., Wladimir. O. O., Diego. C. C., and C. Werner, "Meta-learning based optimization of metabolic pathway data-mining inference system", Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II (IEA/AIE'11), New York, USA, pp. 183-192, 2011.
12. Chen. R., Sivakumar. K., and H. Kargupta, "Collective mining of bayesian networks from heterogeneous data", Knowledge and Information Systems, Vol. 6, No. 2, pp. 164-187, 2004.
13. Tao Li, Shenghuo Zhu and M. Ogihara, "A new distributed data mining model based on similarity", Proceeding of the 2003 ACM symposium on Applied computing (SAC), New York, USA, pp. 432-436, March, 2003.
14. Qin Zhan, Xia Zhang, and Deren Li, "Ontology-based semantic description model for discovery and retrieval of geospatial information", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 32(Part B4), 2008.
15. Zavitsanos. E., Paliouras. G., and G. A. Vouros, "Gold standard evaluation of ontology learning methods through ontology transformation and alignment", IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 11, pp. 1635-1648, 2011.
16. Zouaq. A, Gasevic. D., and M. Hatala, "Towards open ontology learning and filtering", Information Systems, Vol. 36, No. 7, pp. 1064-1081, 2011.
17. Ruiz-Martínez. J. M., Valencia-García. R., T. Fernández-Breis. J., García-Sánchez. F., and R. Martínez-Béjar, "Ontology learning from biomedical natural language documents using UMLS Original", Research Article Expert Systems with Applications, Vol. 38, No. 10, pp. 12365-12378, 2011.
18. Albagli. S., Ben-Eliyahu-Zohary. R., and S. E. Shimony, "Markov network based ontology matching", Journal of Computer and System Sciences, Vol. 78, No. 1, pp. 105-118, 2012.
19. Belhadef. H., "A new bidirectional method for ontologies matching", Procedia Engineering, Vol.23, pp. 558-564, 2011.
20. Juanzi Li, Jie Tang, Yi Li, and Qiong Luo, " RiMOM: a dynamic multistrategy ontology alignment framework", IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 8, pp. 1218-1232, 2009.
21. Ehrig. M., Ontology alignment: bridging the semantic gap, Springer, Germany, 2010.
22. Doan. A., and A. Halevy, "Semantic-integration research in the database community: a brief survey", AI Magazine, Vol. 26, No. 1, pp.83-94, 2005.
23. Shvaiko. P. and J. Euzena, "A survey of schema-based matching approaches", Journal on Data Semantics, Vol. 37, No. 30, pp. 146-171, 2005.
24. Zhichun Wang, Xiao Zhang, Lei Hou, Yue Zhao, Juanzi Li, Yu Qi, and Jie Tang, "RiMOM results for OAEI 2010", OM 2010, Beijing, China, 2010.