Abstract
This paper extends the traditional inductive logic programming (ILP) framework to a ψ-term capable ILP framework. Aït-Kaci’s ψ-terms have interesting and significant properties for markedly widening applicable areas of ILP. For example, ψ-terms allow partial descriptions of information, generalization and specialization of sorts (or types) placed instead of function symbols, and abstract descriptions of data using sorts; they have comparable representation power to feature structures used in natural language processing. We have developed an algorithm that learns logic programs based on -terms, made possible by a bottom-up approach employing the least general generalization (lgg) extended for ψ-terms. As an area of application, we have selected information extraction (IE) tasks in which sort information is crucial in deciding the generality of IE rules. Experiments were conducted on a set of test examples and background knowledge consisting of case frames of newspaper articles. The results showed high precision and recall rates for learned rules for the IE tasks.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
H. Aït-Kaci and R. Nasr, LOGIN: A logic programming language with built-in inheritance, J. Logic Programming, 3, pp.185–215, 1986.
H. Aït-Kaci and A. Podelski, Toward a Meaning of LIFE, PRL-RP-11, Digital Equipment Corporation, Paris Research Laboratory, 1991.
H. Aït-Kaci, B. Dumant, R. Meyer, A. Podelski, and P. Van Roy, The Wild Life Handbook, 1994.
H. Aït-Kaci, A. Podelski and S. C. Goldstein, Order-Sorted Feature Theory Unification, J. Logic Programming, Vol.30, No.2, pp.99–124, 1997.
E. Armengol and E. Plaza, Induction of Feature Terms with INDIE, ECML-97, pp.33–48, 1997.
Birkhoff, G., Lattice Theory, American Mathematical Society, 1979.
M. E. Califf and R. J. Mooney, Relational Learning of Pattern-Match Rules for Information Extraction, ACL-97 Workshop in Natural Language Learning, 1997.
B. Carpenter, The Logic of Typed Feature Structures, Cambridge University Press, 1992.
B. Cestnik, Estimating Probabilities: A Crucial Task in Machine Learning, ECAI-90, pp.147–149, 1990.
S. B. Huffman, Learning Information Extraction Patterns from Examples, Statistical and Symbolic Approaches to Learning for Natural Language Processing, pp. 246–260, 1996.
S. Ikehara, M. Miyazaki, and A. Yokoo, Classification of Language Knowledge for Meaning Analysis in Machine Translations, Transactions of Information Processing Society of Japan, Vol. 34, pp.1692–1704, 1993 (in Japanese).
N. Lavrač and S. Džeroski: Inductive Logic Programming: Techniques and Applications, Ellis Horwood, 1994.
W. Lehnert, C. Cardie, D. Fisher, J. McCarthy, E. Riloff and S. Soderland, University of Massachusetts: MUC-4 Test Results and Analysis, Fourth Message Understanding Conference, pp.151–158, 1992.
S. Muggleton, Inductive Logic Programming, New Generation Computing, 8(4), pp.295–318, 1991.
S. Muggleton and C. Feng, Effcient Induction of Logic Programs, in Inductive Logic Programming, Academic Press, 1992.
E. Plaza, Cases as terms: A feature term approach to the structured representation cases, First Int. Conf. on Case-Based Reasoning, pp. 263–276, 1995.
G. Plotkin, A Note on Inductive Generalization, in B. Jeltzer et al. eds., Machine Intelligence 5, pp.153–163, Edinburgh University Press, 1969.
E. Riloff, Automatically Generating Extraction Pattern from Untagged Text, AAAI-96, pp. 1044–1049, 1996.
Y. Sasaki and M. Haruno, RHB+: A Type-Oriented ILP System Learning from Positive Data, IJCAI-97, pp.894–899, 1997.
Y. Sasaki, Learning of Information Extraction Rules using ILP — Preliminary Report, The Second International Conference on The Practical Application of Knowledge Discovery and Data Mining, pp.195–205, London, 1998.
Y. Sasaki, Applying Type-Oriented ILP to IE Rule Generation, AAAI-99 Workshop on Machine Learning for Information Extraction, pp.43–47,1999.
S. Soderland, D. Fisher, J. Aseltine, W. Lenert, CRYSTAL: Inducing a Conceptual Dictionary, IJCAI-95, pp.1314–1319, 1995.
J. M. Zelle and R. J. Mooney, J. B. Konvisser, Combining Top-down and Bottomup Methods in Inductive Logic Programming, ML-94, pp.343–351, 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sasaki, Y. (1999). Induction of Logic Programs Based on ψ-Terms. In: Watanabe, O., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1999. Lecture Notes in Computer Science(), vol 1720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46769-6_14
Download citation
DOI: https://doi.org/10.1007/3-540-46769-6_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66748-3
Online ISBN: 978-3-540-46769-4
eBook Packages: Springer Book Archive