Knowledge acquisition method from domain text based on theme logic model and artificial neural network

Expert Systems with Applications

Volumn 37, Issue 1, 2010, Pages 267-275

  Wang, Jun ^a   Wu, Yunpeng ^a   Liu, Xuening ^a   Gao, Xiaoying ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Artificial neural network; Domain text; Failure analysis report; Knowledge acquisition; Theme logic model

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DOMAIN KNOWLEDGE; DOMAIN ONTOLOGIES; DOMAIN TEXT; EMPIRICAL RESULTS; LOGIC MODELS; LOGICAL RELATIONS; META-KNOWLEDGE; NEW MODEL; PRE-PROCESSING STEP; SD METHOD; THEME LOGIC MODEL; TRAINING PROCESS; TRAINING SETS;

AVIATION; BACKPROPAGATION; KNOWLEDGE ACQUISITION; MERGERS AND ACQUISITIONS; NEURAL NETWORKS; ONTOLOGY; QUALITY ASSURANCE; SAFETY FACTOR;

FAILURE ANALYSIS;

EID: 70349451244     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.05.009     Document Type: Article

References (38)

1. Buckley, C., & Salton, G. (1995). Optimization of relevance feedback weights. In SIGIR'95: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval (pp. 351-357). New York, NY, USA: ACM.
2. Chandrasekaran B., Josephson J., and Benjamins V. What are ontologies, and why do we need them?. Intelligent Systems and Their Applications, IEEE 14 1 (1999) 20-26
3. Chen, J., Robertsx, C., & Weston, P. (2006). Neuro-fuzzy fault detection and diagnosis for railway track circuits. In: Fault detection, supervision and safety of technical processes (pp. 1366-1371).
4. de Campos L.M., Fernández-Luna J.M., and Huete J.F. Using context information in structured document retrieval: An approach based on influence diagrams. Information Processing and Management 40 5 (2004) 829-847
5. de Castro P.M.S.T., and Fernandes A.A. Methodologies for failure analysis: A critical survey. Materials and Design 25 2 (2004) 117-123
6. Graupe D. Principles of artificial neural networks (2007), World Scientific
7. Gunn A., Sutcliffe G., and Walker D. Knowledge acquisition for natural resource management. Computers and Electronics in Agriculture 23 1 (1999) 71-82
8. Hahn U., and Romacker M. Content management in the syndikate system - How technical documents are automatically transformed to text knowledge bases. Data and Knowledge Engineering 35 2 (2000) 137-159
9. Hassoun, M. (1995). Fundamentals of artificial neural networks, a bradford book. Cambridge, MA, London, England: The MIT Press.
10. Huang S.H., and Xing H. Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets and Systems 132 2 (2002) 233-243
11. Khaw J.F.C., Lim B.S., and Lim L.E.N. Optimal design of neural networks using the taguchi method. Neurocomputing 7 3 (1995) 225-245
12. Kim Y., and Street W.N. An intelligent system for customer targeting: A data mining approach. Decision Support Systems 37 2 (2004) 215-228
13. Lassila O., Swick R., et al. Resource Description Framework (RDF) model and syntax specification (1999)
14. Liao T.W., Zhan Z.-H., and Mount C.R. An integrated database and expert system for failure mechanism identification: Part i - Automated knowledge acquisition. Engineering Failure Analysis 6 6 (1999) 387-406
15. Liao T., Zhan Z., and Mount C. An integrated database and expert system for failure mechanism identification: Part ii - The system and performance testing. Engineering Failure Analysis 6 6 (1999) 407-421
16. Mao W., and Chu W.W. The phrase-based vector space model for automatic retrieval of free-text medical documents. Data and Knowledge Engineering 61 1 (2007) 76-92
17. Maren A., Harston C., and Pap R. Handbook of neural computing applications (1990), Academic Press Inc., Orlando, FL, USA
18. Massey L. On the quality of art1 text clustering. Neural Networks 16 5-6 (2003) 771-778
19. Matwin S., and Szpakowicz S. Text analysis: How can machine learning help. Proceedings of the first conference of the Pacific Association for Computational Linguistics (PACLING) (1993) 33-42
20. Mayer P. Boiler tube failure mechanism recognition - An expert system. CIM Bulletin 83 939 (1990) 92-95
21. Mitchell T.M. Machine learning (2003), China Machine Press
22. Raghavan V.V., and Wong S.K.M. A critical analysis of vector space model for information retrieval. Journal of the American Society for Information Science 37 5 (1986) 279-287
23. Richardson, S. D. (1997). Determining similarity and inferring relations in a lexical knowledge base. Ph.D. Thesis, The City University of New York.
24. Roberts R. Should there be a methodology for failure analysis. Critical Issues in Materials and Mechanical Engineering 47 (1980) 171-184
25. Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning internal representations by error propagation, Parallel distributed processing: Explorations in the microstructure of cognition: Foundations (Vol. 1). Cambridge, MA: MIT Press.
26. Salton, G., Singhal, A., Buckley, C., & Mitra, M. (1996). Automatic text decomposition using text segments and text themes. In: HYPERTEXT'96: Proceedings of the the seventh ACM conference on Hypertext (pp. 53-65). New York, NY, USA: ACM.
27. Salton G., Wong A., and Yang C. A vector space model for information retrieval. Journal of the American Society for Information Science 18 11 (1975) 613-620
28. Salton G., Wong A., and Yang C.S. A vector space model for automatic indexing. Communications of the ACM 18 11 (1975) 613-620
29. Santiago, I. B., Faure, J.-M., & Papadopoulos, Y. (2006). Including systematic faults into fault tree analysis. In Fault detection, supervision and safety of technical processes (pp. 765-770).
30. Sestito, S., & Dillon, T. (1990). Machine learning using single-layered and multi-layered neural networks. Tools for artificial intelligence. In Proceedings of the second international IEEE conference (pp. 269-275).
31. Sestito S., and Dillon T. Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications. Applied Intelligence 1 2 (1991) 157-173
32. Tai X., Ren F., and Kita K. An information retrieval model based on vector space method by supervised learning. Information Processing and Management 38 6 (2002) 749-764
33. Turban E. Decision support and expert systems: Management support systems (1995), Prentice Hall International, London, England
34. Turban E., and Aronson J. Decision Support Systems and Intelligent Systems (2001), Prentice Hall PTR Upper Saddle River, NJ, USA
35. Wang J., Liu X., and Wu Y. Research on knowledge acquisition method from domain text. In: Yu H.-G., and Jiang Y. (Eds). Proceedings of first international conference on modelling and simulation Vol. 4 (2008), World Academic Press 259-262
36. Wei C.-P., Yang C.C., and Lin C.-M. A latent semantic indexing-based approach to multilingual document clustering. Decision Support Systems 45 3 (2008) 606-620
37. Mechanical design manual Editorial Board. (2007). Mechanical design manual (Vol. 5). Beijing: China Machine Press.
38. Zakarian V.L., and Kaiser M.J. An embedded hybrid neural network and expert system in a computer-aided design system. Expert Systems with Applications 16 2 (1999) 233-243