收藏本页

 | 

二维码

 | 

手机版

文献 > 计算机科学 

Distance learning techniques for ontology similarity measuring and ontology mapping 

期刊名称: CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
影响因子: 1.85
网址: https://link.springer.com/journal/10586
作者:  Gao, Wei; Farahani, Mohammad Reza; Aslam, Adnan; Hosamani, Sunilkumar
doi:   10.1007/s10586-017-0887-3
  Download
  收藏
级别:    
高被引论文:    
Highlights:

创新与亮点:

Abstract:

Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

摘要:

关键词:

Ontology; Similarity measuring; Ontology mapping; Distance learning

全 文
中文全文
Cited articles (43)
1.Adams W
2014, DISCRETE OPTIM, V14, P46, DOI 10.1016/j.disopt.2014.07.001
2. Anguita D
2012, NEURAL PROCESS LETT, V36, P275, DOI 10.1007/s11063-012-9235-z
3. Atalay KD
2015, J MANUF SYST, V36, P182, DOI 10.1016/j.jmsy.2014.06.005
4. Azevedo CLB
2015, INFORM SYST, V54, P235, DOI 10.1016/j.is.2015.04.008
5. Bartlett PL
2008, J MACH LEARN RES, V9, P1823
6. Brittain K
2012, STRUCT MULTIDISCIP O, V45, P657, DOI 10.1007/s00158-011-0715-y
7. Candia-Vejar A
2011, RAIRO-OPER RES, V45, P101, DOI 10.1051/ro/2011111
8. Chassein AB
2015, EUR J OPER RES, V244, P739, DOI 10.1016/j.ejor.2015.02.023
9. Craswell N.
2003, TREC 02, P78
10. Dececchi TA
2015, SYST BIOL, V64, P936, DOI 10.1093/sysbio/syv031
11. Ehrgott M
2014, EUR J OPER RES, V239, P17, DOI 10.1016/j.ejor.2014.03.013
12. Gao W
2017, J DIFFER EQU APPL, V23, P100, DOI 10.1080/10236198.2016.1197214
13. Gao W.
2013, J CHEM PHARM RES, V5, P592
14. Gao W.
2011, FUTURE COMMUNICATION, V142, P415, DOI DOI 10.1007/978-3-642-27314-8_56
15. Gao W
2017, COMPUT J, V60, P1289, DOI 10.1093/comjnl/bxw107
16. Gao W
2017, SAUDI J BIOL SCI, V24, P132, DOI 10.1016/j.sjbs.2016.09.001
17. Gao W
2016, CLUSTER COMPUT, V19, P2201, DOI 10.1007/s10586-016-0651-0
18. Gao W
2016, J INTELL FUZZY SYST, V31, P2411, DOI 10.3233/JIFS-169082
19. Gao W
2016, CHAOS SOLITON FRACT, V89, P290, DOI 10.1016/j.chaos.2015.11.035
20. Gao W
2014, COMPUT INTEL NEUROSC, DOI 10.1155/2014/438291
21. Gao W
2012, INFORMATION-TOKYO, V15, P4585
22. [高炜 GAO Wei]
2011, [微电子学与计算机, Microelectronics & Computer], V28, P59
23. GAO Y
2012, INT J MACH LEARNING, V2, P107, DOI DOI 10.7763/IJMLC.2012.V2.97
24. Herrmann-Pillath C
2015, ECOL ECON, V119, P432, DOI 10.1016/j.ecolecon.2014.11.014
25. Tuan HN
2015, J MATH ANAL APPL, V423, P1311, DOI 10.1016/j.jmaa.2014.10.048
26. Huang X.
2011, INT J APPL PHYS MATH, V1, P54, DOI DOI 10.7763/IJAPM.2011.V1.11
27. Kasperski A
2010, EUR J OPER RES, V200, P680, DOI 10.1016/j.ejor.2009.01.044
28. Kasperski A
2009, INFORM PROCESS LETT, V109, P262, DOI 10.1016/j.ipl.2008.10.008
29. Kim HH
2015, INT J MED INFORM, V84, P1099, DOI 10.1016/j.ijmedinf.2015.08.005
30. Muu LD
2014, MATH METHOD OPER RES, V80, P83, DOI 10.1007/s00186-014-0470-0
31. Lee CP
2013, NEURAL COMPUT, V25, P1302, DOI 10.1162/NECO_a_00434
32. Morente-Molinera JA
2015, KNOWL-BASED SYST, V88, P154, DOI 10.1016/j.knosys.2015.07.035
33. Nardi JC
2015, INFORM SYST, V54, P263, DOI 10.1016/j.is.2015.01.012
34. Natarajan K
2014, OPER RES, V62, P160, DOI 10.1287/opre.2013.1212
35. Saberian F
2015, OPER RES LETT, V43, P254, DOI 10.1016/j.orl.2015.02.005
36. Sachnev V
2015, COGN COMPUT, V7, P103, DOI 10.1007/s12559-014-9268-x
37. Sagol G
2015, J GLOBAL OPTIM, V63, P37, DOI 10.1007/s10898-015-0269-4
38. Santipantakis G
2015, KNOWL INF SYST, V45, P491, DOI 10.1007/s10115-014-0807-2
39. Santos G
2015, FOUND SCI, V20, P429, DOI 10.1007/s10699-015-9419-x
40. Sen MU
2013, PATTERN RECOGN LETT, V34, P265, DOI 10.1016/j.patrec.2012.10.008
41. Slota M
2015, ARTIF INTELL, V229, P33, DOI 10.1016/j.artint.2015.07.008
42. Wang YY
2010, 2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VIII, P20, DOI 10.1109/ICNSC.2010.5461557
43. Wimmer H
2015, EXPERT SYST APPL, V42, P8039, DOI 10.1016/j.eswa.2015.04.064
合作伙伴
more>>
         

咨询邮箱:sciencealerts@163.com

版权所有 © 科乐网 2019 Sciencealerts.org

关注订阅号
加入QQ群