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A Transformation-based implementation for CLP with qualification and proximity*

Published online by Cambridge University Press:  25 April 2012

R. CABALLERO ,
M. RODRÍGUEZ-ARTALEJO  and
C. A. ROMERO-DÍAZ
R. CABALLERO
Affiliation:
Departamento de Sistemas Informáticos y Computación, Universidad Complutense, Facultad de Informática, 28040 Madrid, Spain (e-mail: rafa@sip.ucm.es, mario@sip.ucm.es, cromdia@fdi.ucm.es)
M. RODRÍGUEZ-ARTALEJO
Affiliation:
Departamento de Sistemas Informáticos y Computación, Universidad Complutense, Facultad de Informática, 28040 Madrid, Spain (e-mail: rafa@sip.ucm.es, mario@sip.ucm.es, cromdia@fdi.ucm.es)
C. A. ROMERO-DÍAZ
Affiliation:
Departamento de Sistemas Informáticos y Computación, Universidad Complutense, Facultad de Informática, 28040 Madrid, Spain (e-mail: rafa@sip.ucm.es, mario@sip.ucm.es, cromdia@fdi.ucm.es)
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Abstract

Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical logic programming paradigm. However, few of these are designed as extensions of the well-established and powerful Constraint Logic Programming (CLP) scheme for CLP. In a previous work we have proposed the proximity-based qualified constraint logic programming (SQCLP) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple – and working – examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications.

Keywords

constraint logic programmingprogram transformationqualification domains and valuessimilarity and proximity relationsflexible information retrieval
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 14 , Issue 1 , January 2014 , pp. 1 - 63
Copyright
Copyright © Cambridge University Press 2012 

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Footnotes

*

This work has been partially supported by the Spanish projects STAMP (TIN2008-06622-C03-01), PROMETIDOS–CM (S2009TIC-1465) and GPD–UCM (UCM–BSCH–GR58/08-910502).

References

Apt, K. R. 1990. Logic programming. In Handbook of Theoretical Computer Science, Vol. B: Formal Models and Semantics, van Leeuwen, J., Ed. Elsevier and MIT Press, Cambridge, MA, USA, 493–574.Google Scholar
Arcelli Fontana, F. 2002. Likelog for flexible query answering. Soft Computing 7, 107–114.Google Scholar
Arcelli Fontana, F. and Formato, F. 1999. Likelog: A logic programming language for flexible data retrieval. In Proceedings of the 1999 ACM Symposium on Applied Computing (SAC'99). ACM Press, New York, NY, USA, 260–267.Google Scholar
Arcelli Fontana, F. and Formato, F. 2002. A similarity-based resolution rule. International Journal of Intelligent Systems 17, 9, 853–872.Google Scholar
Arenas, P., Fernández, A. J., Gil, A., López-Fraguas, F. J., Rodríguez-Artalejo, M. and Sáenz-Pérez, F. 2007. $\mathcal{TOY}$ , a multiparadigm declarative language (version 2.3.1). In User Manual, Caballero, R. and Sánchez, J., Eds. Accessed 20 February 2012. Available at http://toy.sourceforge.net Google Scholar
Baldwin, J. F., Martin, T. and Pilsworth, B. 1995. Fril-Fuzzy and Evidential Reasoning in Artificial Intelligence. John Wiley, Hoboken, NJ, USA.Google Scholar
Bistarelli, S., Montanari, U. and Rossi, F. 2001. Semiring-based constraint logic programming: Syntax and semantics. ACM Transactions on Programming Languages and Systems 3, 1 (January), 1–29.CrossRefGoogle Scholar
Caballero, R., Rodríguez-Artalejo, M. and Romero-Díaz, C. A. 2008. Similarity-based reasoning in qualified logic programming. In PPDP '08: Proceedings of the 10th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming, ACM, Valencia, Spain, 185–194.Google Scholar
Caballero, R., Rodríguez-Artalejo, M. and Romero-Díaz, C. A. 2009. Qualified computations in functional logic programming. In Logic Programming (ICLP'09), Hill, P. and Warren, D., Eds. LNCS, vol. 5649, Springer-Verlag, Berlin, Germany, 449–463.Google Scholar
Campi, A., Damiani, E., Guinea, S., Marrara, S., Pasi, G. and Spoletini, P. 2009. A fuzzy extension of the XPath query language. Journal of Intelligent Information Systems 33, 3 (December), 285–305.Google Scholar
Dubois, D. and Prade, H. 1980. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, NY, USA.Google Scholar
Freuder, E. C. and Wallace, R. J. 1992. Partial constraint satisfaction. Artificial Intelligence 58, 1–3, 21–70.CrossRefGoogle Scholar
Georget, Y. and Codognet, P. 1998. Compiling semiring-based constraints with CLP(FD,S). In Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming. LNCS, vol. 1520. Springer-Verlag, Berlin, Germany, 205–219.Google Scholar
Gerla, G. 2001. Fuzzy Logic: Mathematical Tools for Approximate Reasoning. Kluwer Academic, Norwell, MA, USA.Google Scholar
Guadarrama, S., Muñoz, S. and Vaucheret, C. 2004. Fuzzy prolog: A new approach using soft constraint propagation. Fuzzy Sets and Systems 144, 1, 127–150.Google Scholar
Hájek, P. 1998. Metamathematics of Fuzzy Logic. Kluwer, Dordrecht, Netherlands.Google Scholar
Höhfeld, M. and Smolka, G. 1988. Definite Relations Over Constraint Languages. Tech. Rep. LILOG Report 53, IBM, Deutschland, Germany.Google Scholar
Ishizuka, M. and Kanai, N. 1985. Prolog-ELF incorporating fuzzy logic. In Proceedings of the 9th International Joint Conference on Artificial Intelligence (IJCAI'85), Joshi, A. K., Ed. Morgan Kaufmann, Los Angeles, CA, USA, 701–703.Google Scholar
Jaffar, J. and Lassez, J. L. 1987. Constraint logic programming. In Proceedings of the 14th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages (POPL'87). ACM Press, New York, NY, USA, 111–119.Google Scholar
Jaffar, J., Maher, M., Marriott, K. and Stuckey, P. J. 1998. Semantics of constraints logic programs. Journal of Logic Programming 37, 1–3, 1–46.Google Scholar
Julián, R., Moreno, G. and Penabad, J. 2009. An improved reductant calculus using fuzzy partial evaluation techniques. Fuzzy Sets and Systems 160, 2, 162–181.Google Scholar
Julián-Iranzo, P., Rubio, C. and Gallardo, J. 2009. Bousi~Prolog: A prolog extension language for flexible query answering. In Proceedings of the Eighth Spanish Conference on Programming and Computer Languages (PROLE 2008), Almendros-Jiménez, J. M., Ed. ENTCS, vol. 248. Elsevier, Gijón, Spain, 131–147.Google Scholar
Julián-Iranzo, P. and Rubio-Manzano, C. 2009a. A declarative semantics for Bousi~Prolog. In PPDP'09: Proceedings of the 11th ACM SIGPLAN Conference on Principles and Practice of Declarative Programming. ACM, Coimbra, Portugal, 149–160.Google Scholar
Julián-Iranzo, P. and Rubio-Manzano, C. 2009b. A similarity-based WAM for Bousi~Prolog. In Bio-Inspired Systems: Computational and Ambient Intelligence (IWANN 2009). LNCS, vol. 5517. Springer, Berlin, Germany, 245–252.Google Scholar
Kifer, M. and Subrahmanian, V. S. 1992. Theory of generalized annotated logic programs and their applications. Journal of Logic Programming 12, 3 & 4, 335–367.Google Scholar
Lee, R. C. T. 1972. Fuzzy logic and the resolution principle. Journal of the Association for Computing Machinery (ACM) 19, 1 (January), 109–119.CrossRefGoogle Scholar
Li, D. and Liu, D. 1990. A Fuzzy Prolog Database System. John Wiley, Hoboken, NJ, USA.Google Scholar
Lloyd, J. W. 1987. Foundations of Logic Programming, 2nd ed., Springer, New York, USA.Google Scholar
Loia, V., Senatore, S. and Sessa, M. I. 2004. Similarity-based SLD resolution and its role for web knowledge discovery. Fuzzy Sets and Systems 144, 1, 151–171.Google Scholar
Medina, J., Ojeda-Aciego, M. and Vojtáš, P. 2001a. Multi-adjoint logic programming with continuous semantics. In Logic Programming and Non-Monotonic Reasoning (LPNMR'01), Eiter, T., Faber, W. and Truszczyinski, M., Eds. LNAI, vol. 2173. Springer-Verlag, Berlin, Germany, 351–364.Google Scholar
Medina, J., Ojeda-Aciego, M. and Vojtáš, P. 2001b. A procedural semantics for multi-adjoint logic programming. In Progress in Artificial Intelligence (EPIA'01), Brazdil, P. and Jorge, A., Eds. LNAI, vol. 2258. Springer-Verlag, Berlin, Germany, 290–297.Google Scholar
Medina, J., Ojeda-Aciego, M. and Vojtáš, P. 2004. Similarity-based unification: A multi-adjoint approach. Fuzzy Sets and Systems 146, 43–62.Google Scholar
Riezler, S. 1998. Probabilistic Constraint Logic Programming. PhD thesis, Neuphilologischen Fakultät del Universität Tübingen, Tübingen, Germany.Google Scholar
Rodríguez-Artalejo, M. and Romero-Díaz, C. A. 2008. Quantitative logic programming revisited. In Functional and Logic Programming (FLOPS'08), Garrigue, J. and Hermenegildo, M., Eds. LNCS, vol. 4989. Springer-Verlag, Ise, Japan, 272–288.Google Scholar
Rodríguez-Artalejo, M. and Romero-Díaz, C. A. 2010a. A declarative semantics for CLP with qualification and proximity. Theory and Practice of Logic Programming, 26th Int'l. Conference on Logic Programming (ICLP'10) Special Issue 10, 4–6, 627–642.Google Scholar
Rodríguez-Artalejo, M. and Romero-Díaz, C. A. 2010b. Fixpoint & Proof-theoretic Semantics for CLP with Qualification and Proximity. Tech. Rep. SIC-1-10 (CoRR abs/1009.1977), Universidad Complutense, Departamento de Sistemas Informáticos y Computación, Madrid, Spain.Google Scholar
Sessa, M. I. 2001. Translations and similarity-based logic programming. Soft Computing 5, 2, 160–170.Google Scholar
Sessa, M. I. 2002. Approximate reasoning by similarity-based SLD resolution. Theoretical Computer Science 275, 1–2, 389–426.Google Scholar
SICS AB. 2010. SICStus Prolog. Accessed 20 February 2012. URL: http://www.sics.se/sicstus Google Scholar
SWI-Prolog . 2010. SWI-Prolog. Accessed 20 February 2012. URL: http://www.swi-prolog.org Google Scholar
van Emden, M. H. 1986. Quantitative deduction and its fixpoint theory. Journal of Logic Programming 3, 1, 37–53.Google Scholar
Vojtáš, P. 2001. Fuzzy logic programming. Fuzzy Sets and Systems 124, 361–370.Google Scholar
Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8, 3, 338–353.CrossRefGoogle Scholar
Zadeh, L. A. 1971. Similarity relations and fuzzy orderings. Information Sciences 3, 2, 177–200.Google Scholar