Efficient Operations in Feature Terms Using Constraint Programming

  • Santiago Ontañón
  • Pedro Meseguer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)


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.


Description Logic Constraint Programming Constraint Satisfaction Problem Inductive Logic Programming Horn Clause 
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  1. 1.
    Aït-Kaci, H.: Description logic vs. order-sorted feature logic. In: DL (2007)Google Scholar
  2. 2.
    Aït-Kaci, H., Podelski, A.: Towards a meaning of LIFE. Tech. Rep. 11, Digital Research Laboratory (1992)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Arcos, J.L.: The NOOS representation language. Ph.D. thesis, Universitat Politècnica de Catalunya (1997)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Carpenter, B.: The Logic of Typed Feature Structures. Cambridge Tracts in Theoretical Computer Science, vol. 32. Cambridge University Press (1992)Google Scholar
  8. 8.
    Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning, 3–23 (2008)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Kuchcinski, K.: Constraint-driven scheduling and resource assignment. ACM Transactions on design Automaton of Electronic Systems 8, 355–383 (2003)CrossRefGoogle Scholar
  12. 12.
    Larson, J., Michalski, R.S.: Inductive inference of vl decision rules. SIGART Bull. (63), 38–44 (1977)CrossRefGoogle Scholar
  13. 13.
    Lavrač, N., Džeroski, S.: Inductive Logic Programming. Techniques and Applications. Ellis Horwood (1994)Google Scholar
  14. 14.
    Maloberti, J., Sebag, M.: Fast theta-subsumption with constraint satisfaction algorithms. Machine Learning 55, 137–174 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Rouveirol, C.: Flattening and saturation: Two representation changes for generalization. Machine Learning 14(1), 219–232 (1994)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Santiago Ontañón
    • 1
  • Pedro Meseguer
    • 1
  1. 1.IIIA-CSICArtificial Intelligence Research Institute, Spanish Scientific Research CouncilBellaterraSpain

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