Discovery of ontologies from knowledge bases

Proceedings of the First International Conference on Knowledge Capture

Volumn , Issue , 2001, Pages 171-178

  Suryanto, Hendra ^a   Compton, Paul ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Knowledge acquisition; Machine learning; Ontology

Indexed keywords

CLASSIFICATION (OF INFORMATION); KNOWLEDGE ACQUISITION; LEARNING SYSTEMS;

ONTOLOGY;

KNOWLEDGE BASED SYSTEMS;

EID: 0035789191     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/500737.500764     Document Type: Conference Paper

References (26)

1. Agrawal, R., H. Mannila, et al. (1996). Fast Discovery of Association Rules, AAAI/MIT Press chapter 12, 307-328.
2. Beydoun, G. and A. Hoffmann (1997). NRDR for the Acquisition of Search Knowledge. 10th Australian Conference on Artificial Intelligence. 1-16.
3. Catlett, J. (1992). Ripple Down Rules as a Mediating Representation in Interactive Induction. Proceedings of the Second Japanese Knowledge Acquisition for Knowledge Based Systems Workshop, Kobe, Japan. 155-170
4. Compton, P. and R. Jansen. (1990). A philosophical basis for knowledge acquisition. Knowledge Acquisition 2: 241-257.
5. Compton, P., P. Preston, et al. (1995). The Use of Simulated Experts in Evaluating Knowledge Acquisition. 9th AAAI-sponsored Banff Knowledge Acquisition for Knowledge Base System Workshop, Canada. 12.1-12.18
6. th International Conference EKAW2000). Springer, Berlin. 380-386.
7. Edwards, G., P. Compton, R. Malor, A. Srinivasan, and L. Lazarus (1993) PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports Pathology 25: 27-34.
8. Flax, Lee. Application of hamming distance between logical formulae to statistical sontingency tables. Technical Report, Computing Departement, Macquarie University, Australia.
9. Gaines, B. R. (1989). Ounce of Knowledge is Worth a Ton of Data: Quantitative Studies of the Trade-off between Expertise and Data based on Statistically Well-founded Empirical Induction, Proceeding of the sixth International Workshop on Machine Learning. San Mateo, California: Morgan Kaufmann, 1989. 156-159
10. Gaines, B. R. (1995). Transforming Rules and Trees into Comprehensible Knowledge Structures, Advance in Knowledge discovery and Data Mining. AAAI/MIT Press. 205-226
11. Gaines, B. R. and P. J. Compton (1992). Induction of Ripple Down Rules. Fifth Australian Conference on Artificial Intelligence, World Scientific, Singapore. Hobart. 349-354
12. Hamming, R. W. Coding and infromation theory. Prentice Hall 1986.
13. Kang, B., P. Compton, et al. (1998). Simulated Expert Evaluation of Multiple Classification Ripple Down Rules. 11th Banff knowledge acquisition for knowledge-based systems workshop, Banff, SRDG Publications, University of Calgary. 1-19
14. Kang, B., Yoshida, et al. (1997). A help desk system with intelligent interface. Applied Artificial Intelligence 11((7-8)): 611-631.
15. Kang, B. H., W. Gambetta, et al. (1996). Verification and Validation with Ripple Down rules. International Journal of Human-Computer Studies 44: 257-269.
16. Kivinen, J., H. Mannila, et al. (1993). Learning Rules with Local Exceptions. European Conference on Computational Theory.
17. Perez, A. G. (1999). Evaluation of Taxonomic Knowledge in Ontologies and Knowledge Bases. KAW'99, Twelfth Workshop on Knowledge Acquisition, Modeling and Management, Voyager Inn, Banff, Alberta, Canada. 6.1.1-6.1.18
18. Ramadan, Z., P. Compton, et al. (1998). From Multiple Classification RDR to Configuration RDR. 11th Banff Knowledge Acquisition for Knowledge Base System Workshop, Canada. 1-19
19. Richards, D. and P. Compton (1997). Uncovering the conceptual models in RDR KBS. International Conference on Conceptual Structures ICCS'97, Seattle, Springer Verlag. 198-212
20. Richards, D. (1998). Ripple Down Rules with Formal Concept Analysis: A Comparison to Personal Construct Psychology. Proceedings of KAW'98, Eleventh Workshop on Knowledge Acquisition, Modelling and Management, Banff, Alberta, Canada. 1-20
21. Scheffer, T. (1996). Algebraic foundations and improved methods of induction or ripple-down rules. 2nd Pacific Rim Knowledge Acquisition Workshop. 279-292
22. Schreiber, G., B. Wielinga, et al. (1993). KADS A Principled Approach to Knowledge-Based System Development, Academic Press.
23. Siromoney, A. and R. Siromoney (1993). Variations and Local Exception in Inductive Logic Programming. Machine Intelligence - Applied Machine Intelligence. K. Furukawa, D. Michie and S. Muggleton. 14: 213 - 234.
24. Suryanto, H., D. Richards, et al. (1999). The automatic compression of Multiple Classification Ripple Down Rule Knowledged Based Systems: Preliminary Experiments. Knowledge-based Intelligence Information Engineering Systems, Adelaide, South Australia, IEEE. 203-206
25. Suryanto, H., Compton, P. (2000). Discovery of Class Relations in Exception Structured Knowledge Bases. International Conference on Conceptual Structures, ICCS 2000. 113-126
26. William E. Grosso, H. E., Ray W Fergeson (1999). Knowledge Modeling at the Millenium (The Design and Evolution of Protege-2000). Twelfth Workshop on Knowledge Acquisition, Modeling and Management, Voyager Inn, Banff, Alberta, Canada. 7.4.1 - 7.4.36