Relational knowledge discovery in a chinese character database

Applied Artificial Intelligence

Volumn 12, Issue 5, 1998, Pages 455-488

  Zucker, Jean Daniel ^a   Ganascia, Jean Gabriel ^a   Bournaud, Isabelle ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COMPUTER SYSTEMS PROGRAMMING; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; LOGIC PROGRAMMING; RELATIONAL DATABASE SYSTEMS;

INDUCTIVE LOGIC PROGRAMMING (ILP); RELATIONAL KNOWLEDGE DISCOVERY;

KNOWLEDGE ACQUISITION;

EID: 0032120499     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/088395198117712     Document Type: Article

References (69)

1. Aha, D. W. 1992. Relating relational learning algorithms. In Inductive logic programming, ed. S. Muggleton, pp. 233-259. London: Harcourt Brace Jovanovich.
2. Amin, A., C. P. Chen, C. Sammut, and K. C. Sum. 1994. Learning to recognize hand-printed Chinese characters using inductive logic programming. In Proc. Fourth International W orkshop on Inductive Logic programming (ILP-94), ed. S. Wrobel, Bonn, Germany, pp. 263-271, Sankt Augustin: Gesellschaft GMD-Studien Nr 237.
3. Ann, T. K. 1987. Cracking the Chinese puzzles, Hong-Kong: Stockflows Co.
4. Bellassen, J. 1989. Methode d’initiation a la Langue et a l’écriture Chinoises. Paris: La Compagnie.
5. Bergadano, F., A. Giordana, and L. Saitta, 1991. Machine learning: An integrated framework and its application. Chichester, England: Ellis Horwood.
6. Bergadano, F., and D. Gunetti. 1993. An interactive system to learn functional logic programs. In Proc. 13th International J oint Conference on Artificial Intelligence, Chambery, France.
7. Birkhoff, G. 1967. Lattice theory, 3rd ed. Providence, Rhode Island: American Mathematical Society.
8. Blockeel, H., and L. De Raedt. 1996. Relational knowledge discovery in databases. In Proc. Data Mining with Inductive Logic Programming (MLnet Familiarization Workshop), ed. S. Muggleton, Bari, Italy, pp. 1-13, Stockholm: Royal InstituteofTechnology.
9. Brachman, R., and T. Anand, 1996. The process of knowledge discovery in databases: A human centered approach. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, pp. 37-58. Boston: MIT Press.
10. Brockhausen, P., and K. Morik. 1996. Direct access of an ILP algorithm to a database management system. In Proc. Data Mining with inductive Logic Programming (MLnet Familiarization Workshop), Bari, Italy.
11. Cestnik, B., I. Kokonenko, and I. Bratko. 1987. ASSISTANT 86: A knowledge elicitation tool for sophisticated users. In Progress in machine learning, eds. I. Bratko, and N. Lavrac, pp. 31-45. Wilmslow, England: Sigma Press.
12. Chaffin, R., and D. Herrmann. 1988. The nature of semantic relations. In Relational models of the lexicon. ed. M. Evens, pp. 289-334. Cambridge: Cambridge University Press.
13. Cohen, W. 1993a. Learnability of restricted logic programs. In Proc. IJCAI, Workshop on ILP, ed. S. Muggleton, Chambery, France (Technical Report IJS-DP-6707 of the Josef Stefan Institute, Ljubljana, Slovenia).
14. Cohen, W. 1993b. Rapid prototyping of ILP systems using explicit bias. In Proc. IJCAI 93WS, eds. F. Bergadano, L. De Raedt, S. Matwin, and S. Muggleton, Chambery, France, pp. 24-35.
15. Cohen, W. 1994. Pac-learning nondeterminate clauses. In Proc. Twelfth National Conference on Artificial Intelligence (AAAI ’94), Seattle, Washington, pp. 676-681. Boston: MIT Press.
16. Corruble, V., and J.-G. Ganascia. 1997. Induction and the discovery of the causes of scurvy: A computational reconstruction. Artificial Intelligence 91:205-223.
17. De Raedt, L., and M. Bruynooghe. 1992. An overview of the interactive concept-learner and theory revisor CLINT. In Inductive logic programming, ed. S. Muggleton, pp. 63-192. London: Harcourt Brace Jovanovich.
18. De Raedt, L., and W. Van Laer. 1995. Inductive constraint logic. In Proc. 5th Workshop on Algorithmic Learning Theory. Berlin: Springer-Verlag.
19. Dehaspe, L., L. De Raedt, and W. Van Laer. 1995. CLAUDE The Clausal Discovery Engine: User’s guide. Heverlee, Belgium. Dept. ofComputer Science, Katholieke Universiteit Leuven.
20. Dietterich, T., R. Lathrop, and T. Lozano-Perez. 1996. Solving the multiple-instance problem with axisparallel rectangles. Journal of Artificial Intelligence 89(1-2): 31-71.
21. Ding, X. 1952. Xiandai Hanzi Ji Qi Gaige de Tujing: Chinese characters and means to reform them. Zhongguo yuwen (Chinese language and writing). Beijing.
22. Dzeroski, S. 1996. Inductive logic programming and knowledge discovery in databases. In Advances in knowledge discovery and data mining, eds. U. M. Fayad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy. San Diego, California: Academic Press.
23. Dzeroski, S., S. Muggleton, and S. Russell. 1992. PAC-learning of determinate logic programs. In Proc. Of the Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania.
24. Dzeroski, S., S. Shulze-Kremer, and K. Heidtke. 1996. Applying ILP to diterprene structure elucidation from 13C NMR spectra. In Proc. Data Mining with Inductive Logic Programming (MLnet Familiarization Workshop). Bari, Italy.
25. Fayad, U. M., G. Piatetsky-Shapiro, and P. Smyth. 1996a. Knowledge discovery and data mining: Towards a unifying framework. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pp. 82-88. Portland, Oregon: AAAI Press.
26. Fayad, U. M., G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy (eds.). 1996b. Advances in knowledge discovery and data mining, San Diego, California: Academic Press.
27. Fensel, D., M. Zickwolff, and M. Wiese. 1995. Are substitutions the better examples? Learning complete set of clauses with Frog. In Proc. 5th International Workshop on Inductive Logic Programming ILP-95, ed. L. De Raedt, Leuven, Belgium, pp. 453-474.
28. Ganascia, J.-G. 1987. CHARADE: A rule system learning system. In Proc. Tenth International Joint Conference in Artificial Intelligence, Milan, Italy, pp. 345-347. San Mateo, Calif.: Morgan Kaufmann.
29. Ganascia, J.-G. 1991. Deriving the learning bias from rule properties. Machine Intelligence 12.
30. Ganascia, J.-G. 1993. TDIS: An algebraic formalization. In Proc. 13th International Joint Conference on Artificial Intelligence, Chambery, France, pp. 1008-1013. San Mateo, Calif.: Morgan Kaufmann.
31. Giordana, A., F. Neri, L. Saitta, and M. Botta. 1997. Integrating multiple learning strategies in first order logics. Machine Learning 27:209-240.
32. Guarino, N., S. Pribbenow, and L. Vieu. 1994. Parts and wholes: Conceptual part-whole relations and formal mereology. In Proc. Workshop W2 at the 11th European Conference on Artificial Intelligence, Amsterdam, The N etherlands.
33. Haussler, D. 1989. Learning conjunctive concepts in structural domains. Machine Learning 4:7-40.
34. Hayes-Roth, F., and J. McDermott. 1978. An interference matching technique for inducing abstractions. Communications of the ACM 21(5): 401-410.
35. Iris, M., B. Litowitz, and M. Evens. 1988. Problems of the part-whole relation. In Relational models of the lexicon, ed. M. Evens, pp. 261-288. Cambridge: Cambridge University Press.
36. Karlgren, B. 1971. Sound andsymbol in Chinese. Hong Kong.
37. Kietz, J.-U., and S. Wrobel. 1992. Controlling the complexity of learning in logic through syntactic and task-oriented models. In Inductive logic programming, ed. S. Muggleton, pp. 335-359. London: Har-court Brace Jovanovich.
38. Kodratoff, Y., and J.-G. Ganascia. 1986. Improving the generalization step in learning. In Machine learning: An artificial intelligence approach, eds. J. Carbonell and T. Mitchell, pp. 215-244. San Mateo, California: Morgan Kaufmann.
39. Lavrac, N., and S. Dzeroski. 1994. Inductive logic programming: Techniques and applications. Chichester, England: Ellis Horwood.
40. Lavrac, N., S. Dzeroski, and M. Grobelnik. 1991. Learning nonrecursive definitions of relations with UNIS. In Proc. European Working Session on Learning ’91, Porto, Portugal, pp. 265-281.
41. Levesque, H., and R. Brachman. 1985. A fundamental trade-off in knowledge representation and reasoning (final version). In Readings in artificial intelligence, eds. R. Brachman and H. Levesque, pp. 41-71. San Mateo, California: Morgan Kaufmann.
42. Michalski, R., I. Mozetic, J. Hong, and N. Lavrac. 1986. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Proc. 5th National Conference on Artificial Intelligence (AAAI’86), 11-15 August, Philadelphia, Pennsylvania, pp. 1041-1045. San Mateo, Calif.: Morgan Kaufmann.
43. Mozetic, I. 1985. NEWGEM: Program for learning from examples. UIUCDS-F-85-949, Department of Computer Science, University of Illinois at Urbana.
44. Muggleton, S., and L. De Raedt. 1994. Inductive logic programming: Theory and methods. Journal of Logic Programming 19(20): 629-679.
45. Muggleton, S., and C. Feng. 1992. Efficient induction of logic program. In Inductive logic programming, ed. S. Muggleton, pp. 281-298. London: Harcourt Brace Jovanovich.
46. Muggleton, S., and A. Srinivasan. 1994. Mode-directed inverse resolution. In Machine intelligence, eds. D. Michie, K. Furukawa, and S. Muggleton. Oxford University Press.
47. Neri, F., and L. Saitta. 1994. Knowledge representation in machine learning. In Proc. European Conference on Machine Learning, Catania, Italy, Lecture notes on Artificial Intelligence, 784, 200-227.
48. Norman, J. 1988. The modern standard language I. In Chinese, pp. 133-151. Cambridge: Cambridge University Press.
49. Pazzani, M. J., C. A. Brunk, and G. Silverstein. 1991. A knowledge intensive approach to relational concept learning. In Proc. 8th IWML, eds. L. A. Birnbaum and G. C. Collins, pp. 432-436. San Mateo, Calif.: Morgan Kaufmann.
50. Peng, T. H. 1990. “Hanyu Pinyin” (romanized Chinese phonetics). Times, Edition Pte Ltd., Hong Kong.
51. Pfahringer, B., and J. Furnkranz. 1996. Data mining with inductive logic programming (ILP for KDD). In Proc. MLnet Familiarization Workshop, Bari, Italy.
52. Piatetsky-Shapiro, G., R. Brachman, T. Khabaza, W. Kloesgen, and E. Simoudis. 1996. An overview of issues in developing industrial data mining and knowledge discovery applications. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon. AAAI Press.
53. Pompe, U., I. Kokonenko, and T. Makse. 1996. An application of ILP in a musical database: Learning to compose the two-voice counterpoint. In Proc. Data Mining with Inductive Logic Programming (MLnet Familiarization Workshop), Bari, Italy, pp. 1-11.
54. Quinlan, J.-R. 1990. Learning logical definitions from relations. Machine Learning 5: 239-266.
55. Rouveirol, C. 1994. Declarative bias workshop. In Proc. MLnet Familiarization Workshops, Catania, Italy.
56. Russell, S. J., and B. N. Grosof. 1990. Declarative bias: An overview. In Change of representation and inductive bias, ed. D. Benjamin, pp. 267-308. Boston, Massachusetts: Kluwer Academic.
57. Saitta, L., and F. Neri. 1998. Learning in the “real world.” Machine Learning (in press).
58. Sebag, M., and C. Rouveirol. 1997. Tractable induction and classification in first order logic. In Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJ CAI’97), pp. 888-893, 23-29 August, Nagoya, Japan. San Mateo, Calif.: Morgan Kaufmann.
59. Semeraro, G., F. Esposito, and D. Malerba. 1996. Ideal refinement of datalog programs. In Logic program synthesis and transformation, ed. M. Proietti, pp. 120-136. New York: Springer-Verlag.
60. Silverstein, G., and M. J. Pazzani. 1991. Relational cliches: Constraining constructive induction during relational learning. In Proc. 8th IWML, eds. L. A. Birnbaum and G. C. Collins, pp. 447-451. San Mateo, Calif.: Morgan Kaufmann.
61. Sowa, J. F. 1991. Part I: Issues in knowledge representation. In Principles of semantic networks: Exploration in the representation of knowledge, ed. J. F. Sowa. San Mateo, California: Morgan Kaufmann.
62. Srinivasan, A., S. Muggleton, R. D. King, and M. E. Sternberg. 1997. The predictive toxicology evalu-atio n challenge. In Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI’97), pp. 4-9, 23-29 August, Nagoya, Japan. San Mateo, Calif.: Morgan Kaufmann.
63. Srinivasan, A., S. Muggleton, M. E. Sternberg, and R. D. King. 1994. Mutagenesis: ILP experiments in a non-determinate biological domain. In Proc. Fourth International Workshop on Inductive Logic Programming (ILP-94), Bonn, Germany, pp. 217-232. Sankt Augustin: Gesellschaft fur Mathematik und Datenverarbeitung.
64. Suen, C. 1972. Computational analysis of Mandarin Birkhauser.
65. Winston, M. E., R. Chaffin, and D. J. Hermann. 1987. A taxonomy of part-whole relations. Cognitive Science 11:417-444.
66. Wrobel, S., and S. Dzeroski. 1995. The ILP description learning problem: Towards a general-model level definition of data mining in ILP. In Proc. FGML95: Annual Workshop of the GI Special Interest Group on Machine Learning.
67. Zhao, Y. R. 1948. Mandarin primer. Cambridge, Massachusetts: Harvard University Press.
68. Zucker, J.-D. 1996. Appariements et changements de représentation pour l’apprentissage symbolique. Thèse de Docteur en Sciences, Spécialité Informatique, Université Paris VI.
69. Zucker, J.-D., and J.-G. Ganascia. 1996. Changes of representation for efficient learning in structural domains. In Proc. International Conference in Machine Learning (ICML, 96), pp. 543-551, 3-6 July, Bari, Italy. San Mateo, Calif.: Morgan Kaufmann.