Abstract
Feature Terms are a generalization of first-order terms that have been introduced in theoretical computer science in order to formalize object-oriented capabilities of declarative languages, and which have been recently receiving increased attention for their usefulness in structured machine learning applications. The main obstacle with feature terms (as well as other formal representation languages like Horn clauses or Description Logics) is that the basic operations like subsumption have a very high computational cost. In this paper we model subsumption, antiunification and unification using constraint programming (CP), solving those operations in a more efficient way than using traditional methods.
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
Aït-Kaci, H.: Description logic vs. order-sorted feature logic. In: DL (2007)
Aït-Kaci, H., Podelski, A.: Towards a meaning of LIFE. Tech. Rep. 11, Digital Research Laboratory (1992)
Aït-Kaci, H., Sasaki, Y.: An Axiomatic Approach to Feature Term Generalization. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 1–12. Springer, Heidelberg (2001)
Arcos, J.L.: The NOOS representation language. Ph.D. thesis, Universitat Politècnica de Catalunya (1997)
Armengol, E., Plaza, E.: Lazy learning for predictive toxicology based on a chemical ontology. In: Artificial Intelligence Methods and Tools for Systems Biology, vol. 5, pp. 1–18 (2005)
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)
Carpenter, B.: The Logic of Typed Feature Structures. Cambridge Tracts in Theoretical Computer Science, vol. 32. Cambridge University Press (1992)
Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning, 3–23 (2008)
Ferilli, S., Fanizzi, N., Di Mauro, N., Basile, T.M.: Efficient theta-subsumption under object identity. In: Workshop AI*IA 2002, pp. 59–68 (2002)
Hoder, K., Voronkov, A.: Comparing unification algorithms in first-order theorem proving. In: Proc. 32th German conf on Advances in AI, pp. 435–443 (2009)
Kuchcinski, K.: Constraint-driven scheduling and resource assignment. ACM Transactions on design Automaton of Electronic Systems 8, 355–383 (2003)
Larson, J., Michalski, R.S.: Inductive inference of vl decision rules. SIGART Bull. (63), 38–44 (1977)
Lavrač, N., Džeroski, S.: Inductive Logic Programming. Techniques and Applications. Ellis Horwood (1994)
Maloberti, J., Sebag, M.: Fast theta-subsumption with constraint satisfaction algorithms. Machine Learning 55, 137–174 (2004)
Ontañón, S., Plaza, E.: On Similarity Measures Based on a Refinement Lattice. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS, vol. 5650, pp. 240–255. Springer, Heidelberg (2009)
Plaza, E.: Cases as Terms: A Feature Term approach to the Structured Representation of Cases. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010, pp. 265–276. Springer, Heidelberg (1995)
Rouveirol, C.: Flattening and saturation: Two representation changes for generalization. Machine Learning 14(1), 219–232 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ontañón, S., Meseguer, P. (2012). Efficient Operations in Feature Terms Using Constraint Programming. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-31951-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31950-1
Online ISBN: 978-3-642-31951-8
eBook Packages: Computer ScienceComputer Science (R0)