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A new calculus for semantic matching

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 982))

Abstract

In this paper, we present Reverse Restructuring, a new calculus for solving the semantic matching problem. For narrowing, advanced selection rules are commonly seen as an appropriate method to reduce the search space. Our approach to design a special calculus for special goals is another way of reducing the efficiency defects of narrowing. Reverse Restructuring constructs derivations in the reverse direction by guessing terms from which an already known term might be derived. To this end, the rules of the underlying term rewriting system are also applied in the reverse direction, i.e. from right to left. We show the soundness and completeness of this calculus and demonstrate its efficiency for an important class of problems.

This work is supported by the DFG-project: “Abstrakte Inferenzmaschine” under Az. Gi 178/1 -2

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References

  1. S. Antoy, R. Echahed, and M. Hanus. A needed narrowing strategy. In Proceedings of the 21st ACM Symposium on Principles of Programming Languages, pages 268–278. ACM Press, 1994.

    Google Scholar 

  2. B. Bütow and S. Thesing. Reverse restrucuring: Another method of solving algebraic equations. Forschungsberichte der Technischen Fakultät, Abteilung Informationstechnik, Report 94-07, Universität Bielefeld, 1994.

    Google Scholar 

  3. B. Bütow and S. Thesing. PaNaMa user's manual. Forschungsberichte der Technischen Fakultät, Abteilung Informationstechnik, Report 95-01, Universität Bielefeld, 1995.

    Google Scholar 

  4. N. Dershowitz and J.-P. Jouannaud. Rewrite systems. In J. van Leuwen, editor, Handbook of Theoretical Computer Science, volume B, pages 243–320. Elsevier Science Publisher B.V., 1990.

    Google Scholar 

  5. N. Dershowitz, S. Mitra, and G. Sivakumar. Decidable matching for convergent systems. In Proceedings of the 11th Conference on automatic deduction (CADE), pages 133–146, 1992.

    Google Scholar 

  6. N. Dershowitz and G. Sivakumar. Solving goals in equational languages. In S. Kaplan and J.-P. Jouannaud, editors, Proceedings of the 1st International Workshop on Conditional Term Rewriting Systems, volume 308 of Lecture Notes in Computer Science, pages 45–55, Orsay, France, July 1987.

    Google Scholar 

  7. H. Emmelmann. Codeselektion mit regulär gesteuerter Termersetzung. PhD thesis, Universität Karlsruhe, 1994.

    Google Scholar 

  8. H. Emmelmann, F.-W. Schröer, and R. Landwehr. BEG — a generator for efficient back ends. In Sigplan '89 Conference on Programming Language Design and Implementation, volume 24, pages 227–237. Sigplan Notices, 1989.

    Google Scholar 

  9. C. Fraser, R. Henry, and T. Proebsting. BURG — fast optimal instruction selection and tree parsing. ACM Sigplan Notices, 27:pages 68–76, 1992.

    Google Scholar 

  10. R.W. Floyd. Algorithm 97. Communications of the ACM, 5(6):page 345, 1962.

    Google Scholar 

  11. R. Giegerich. Code selection by inversion of order-sorted derivors. Theoretical Computer Science, 73:pages 177–211, 1990.

    Google Scholar 

  12. M. Hanus. The integration of functions into logic programming: From theory to practice. Journal of Logic Programming, 19&20:pages 583–628, 1994.

    Google Scholar 

  13. J.W. Klop. Term rewriting systems. In S. Abramsky, D. Gabbay, and T. Maibaum, editors, Handbook of Logic in Computer Science, volume 2, pages 1–116. Oxford University Press, 1992.

    Google Scholar 

  14. S. Mitra. Top-down equation solving and extensions to associative and commutative theories. Masters thesis, Department of Computer and Information Sciences, University of Delaware, 1990.

    Google Scholar 

  15. S. Mitra. Semantic Unification for Convergent Systems. PhD thesis, University of Illinois, 1994.

    Google Scholar 

  16. A. Martelli and U. Montanari. An efficient unification algorithm. ACM Transactions on Programming Languages and Systems (TOPLAS), 4(2):pages 258–282, 1982.

    Google Scholar 

  17. A. Martelli, C. Moiso, and G.F. Rossi. Lazy unification algorithms for canonical rewrite systems. In H. Ait-Kaci and M. Nivat, editors, Resolution of Equations in Algebraic Strucutures, pages 245–274, New York, 1989. Academic Press.

    Google Scholar 

  18. S. Warshall. A theorem on boolean matrices. Journal of the ACM, 9(1):pages 11–12, 1962.

    Google Scholar 

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Manuel Hermenegildo S. Doaitse Swierstra

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© 1995 Springer-Verlag Berlin Heidelberg

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Bütow, B., Giegerich, R., Ohlebusch, E., Thesing, S. (1995). A new calculus for semantic matching. In: Hermenegildo, M., Swierstra, S.D. (eds) Programming Languages: Implementations, Logics and Programs. PLILP 1995. Lecture Notes in Computer Science, vol 982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026815

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  • DOI: https://doi.org/10.1007/BFb0026815

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60359-7

  • Online ISBN: 978-3-540-45048-1

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