Skip to main content
SpringerLink
Log in

Dyna: Extending Datalog for Modern AI

  • Conference paper
Datalog Reloaded (Datalog 2.0 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6702))

Included in the following conference series:

Abstract

Modern statistical AI systems are quite large and complex; this interferes with research, development, and education. We point out that most of the computation involves database-like queries and updates on complex views of the data. Specifically, recursive queries look up and aggregate relevant or potentially relevant values. If the results of these queries are memoized for reuse, the memos may need to be updated through change propagation. We propose a declarative language, which generalizes Datalog, to support this work in a generic way. Through examples, we show that a broad spectrum of AI algorithms can be concisely captured by writing down systems of equations in our notation. Many strategies could be used to actually solve those systems. Our examples motivate certain extensions to Datalog, which are connected to functional and object-oriented programming paradigms.

This chapter has been condensed for publication; the full version is available as [22]. This material is based on work supported by the National Science Foundation under Grants No. 0347822 and 0964681 to the first author, and by a graduate fellowship to the second author from the Human Language Technology Center of Excellence, Johns Hopkins University.We thank Wren N. G. Thornton and John Blatz for many stimulating discussions. We also thank Yanif Ahmad, Adam Teichert, Jason Smith, Nicholas Andrews, and Veselin Stoyanov for timely comments on the writing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acar, U.A., Ley-Wild, R.: Self-adjusting computation with Delta ML. In: Koopman, P.W.M., Plasmeijer, R., Swierstra, S.D. (eds.) AFP 2008. LNCS, vol. 5832, pp. 1–38. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Ahmad, Y., Koch, C.: DBToaster: A SQL compiler for high-performance delta processing in main-memory databases. In: Proc. of VLDB, pp. 1566–1569 (2009)

    Google Scholar 

  3. Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., Mohri, M.: OpenFST: A general and efficient weighted finite-state transducer library. In: Holub, J., Žďárek, J. (eds.) CIAA 2007. LNCS, vol. 4783, pp. 11–23. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  4. Apt, K.R., Blair, H.A., Walker, A.: Towards a theory of declarative knowledge. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, ch. 2. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  5. Berg-Kirkpatrick, T., Bouchard-Côté, A., DeNero, J., Klein, D.: Painless unsupervised learning with features. In: Proc. of NAACL, pp. 582–590. ACL (2010)

    Google Scholar 

  6. Bidoit, N., Hull, R.: Minimalism, justification and non-monotonicity in deductive databases. Journal of Computer and System Sciences 38(2), 290–325 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  7. Breck, E.: zymake: A computational workflow system for machine learning and natural language processing. In: Software Engineering, Testing, and Quality Assurance for Natural Language Processing, SETQA-NLP 2008, pp. 5–13. ACL (2008)

    Google Scholar 

  8. Brodie, M.L.: Future Intelligent Information Systems: AI and Database Technologies Working Together. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  9. Burstall, R.M., Collins, J.S., Popplestone, R.J.: Programming in POP-2. Edinburgh University Press, Edinburgh (1971)

    MATH  Google Scholar 

  10. Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering 1, 146–166 (1989)

    Article  Google Scholar 

  11. Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)

    Book  Google Scholar 

  12. Clark, K.L.: Negation as failure. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 293–322. Plenum, New York (1978)

    Chapter  Google Scholar 

  13. Cohen, S.B., Simmons, R.J., Smith, N.A.: Products of weighted logic programs. Theory and Practice of Logic Programming (2010)

    Google Scholar 

  14. Cohen, S., Nutt, W., Serebrenik, A.: Algorithms for rewriting aggregate queries using views. In: Masunaga, Y., Thalheim, B., Štuller, J., Pokorný, J. (eds.) ADBIS 2000 and DASFAA 2000. LNCS, vol. 1884, pp. 65–78. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  15. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  16. Courtney, A., Elliott, C.: Genuinely functional user interfaces. In: 2001 Haskell Workshop (2001)

    Google Scholar 

  17. The functional logic language Curry, http://www.informatik.uni-kiel.de/~curry/

  18. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Communications of the ACM 5(7), 394–397 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  19. Dechter, R.: Constraint Processing. Morgan Kaufmann, San Francisco (2003)

    MATH  Google Scholar 

  20. Eisner, J.: Parameter estimation for probabilistic finite-state transducers. In: Proc. of ACL, pp. 1–8 (2002)

    Google Scholar 

  21. Eisner, J., Blatz, J.: Program transformations for optimization of parsing algorithms and other weighted logic programs. In: Wintner, S. (ed.) Proc. of FG 2006: The 11th Conference on Formal Grammar, pp. 45–85. CSLI Publications, Stanford (2007)

    Google Scholar 

  22. Eisner, J., Filardo, N.W.: Dyna: Extending Datalog for modern AI (full version). Tech. rep., Johns Hopkins University (2011); Extended version of the present paper, http://dyna.org/Publications

  23. Eisner, J., Goldlust, E., Smith, N.A.: Compiling comp ling: Weighted dynamic programming and the Dyna language. In: Proc. of HLT-EMNLP, pp. 281–290. Association for Computational Linguistics (2005)

    Google Scholar 

  24. Eisner, J., Kornbluh, M., Woodhull, G., Buse, R., Huang, S., Michael, C., Shafer, G.: Visual navigation through large directed graphs and hypergraphs. In: Proc. of IEEE InfoVis, Poster/Demo Session, pp. 116–117 (2006)

    Google Scholar 

  25. Elliott, C., Hudak, P.: Functional reactive animation. In: International Conference on Functional Programming (1997)

    Google Scholar 

  26. Felzenszwalb, P.F., McAllester, D.: The generalized A* architecture. J. Artif. Int. Res. 29(1), 153–190 (2007)

    MathSciNet  MATH  Google Scholar 

  27. Fidler, S., Boben, M., Leonardis, A.: Learning hierarchical compositional representations of object structure. In: Dickinson, S., Leonardis, A., Schiele, B., Tarr, M.J. (eds.) Object Categorization: Computer and Human Vision Perspectives, Cambridge University Press, Cambridge (2009)

    Google Scholar 

  28. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proc. of ACL, pp. 363–370. ACL (2005)

    Google Scholar 

  29. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proc. of the 5th International Conference and Symposium Logic Programming, pp. 1070–1080 (1988)

    Google Scholar 

  30. Goodman, J.: Semiring parsing. Computational Linguistics 25(4), 573–605 (1999)

    MathSciNet  Google Scholar 

  31. Green, T.J., Karvounarakis, G., Tannen, V.: Provenance semirings. In: Proc. of PODS, pp. 31–40 (2007)

    Google Scholar 

  32. Griewank, A., Corliss, G. (eds.): Automatic Differentiation of Algorithms. SIAM, Philadelphia (1991)

    Google Scholar 

  33. Guo, H.-F., Gupta, G.: Simplifying dynamic programming via tabling. In: Jayaraman, B. (ed.) PADL 2004. LNCS, vol. 3057, pp. 163–177. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  34. Gupta, A., Mumick, I.S.: Maintenance of materialized views: Problems, techniques, and applications. IEEE Data Eng. Bull. 18(2), 3–18 (1995)

    Google Scholar 

  35. Hinton, G.: Products of experts. In: Proc. of ICANN, vol. 1, pp. 1–6 (1999)

    Google Scholar 

  36. Johnson, M.: Transforming projective bilexical dependency grammars into efficiently-parsable CFGs with unfold-fold. In: Proc. of ACL, pp. 168–175 (2007)

    Google Scholar 

  37. Kemp, D.B., Stuckey, P.J.: Semantics of logic programs with aggregates. In: Proc. of the International Logic Programming Symposium, pp. 338–401 (1991)

    Google Scholar 

  38. Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. Journal of Logic Programming 12(4), 335–368 (1992)

    Article  MathSciNet  Google Scholar 

  39. Klein, D., Manning, C.D.: A* parsing: Fast exact Viterbi parse selection. In: Proc. of HLT-NAACL (2003)

    Google Scholar 

  40. Kline, M.: Mathematics in the modern world; readings from Scientific American. With introductions by Morris Kline. W.H. Freeman, San Francisco (1968)

    Google Scholar 

  41. LogicBlox: Datalog for enterprise applications: from industrial applications to research (2010), http://www.logicblox.com/research/presentations/arefdatalog20.pdf , presented by Molham Aref at Datalog 2.0 Workshop

  42. LogicBlox: Modular and reusable Datalog (2010), http://www.logicblox.com/research/presentations/morebloxdatalog20.pdf , presented by Shan Shan Huang at Datalog 2.0 Workshop

  43. Loo, B.T., Condie, T., Garofalakis, M.N., Gay, D.E., Hellerstein, J.M., Maniatis, P., Ramakrishnan, R., Roscoe, T., Stoica, I.: Declarative networking. Commun. ACM 52(11), 87–95 (2009)

    Article  Google Scholar 

  44. Marek, V., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K., Marek, V., Truszczyński, M., Warren, D. (eds.) The Logic Programming Paradigm: A 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  45. McAllester, D.A.: On the complexity analysis of static analyses. J. ACM 49(4), 512–537 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  46. The Mercury Project, http://www.cs.mu.oz.au/research/mercury/index.html

  47. Minnen, G.: Magic for filter optimization in dynamic bottom-up processing. In: ACL, pp. 247–254 (1996)

    Google Scholar 

  48. Mohr, R., Henderson, T.: Arc and path consistency revised. Artificial Intelligence 28, 225–233 (1986)

    Article  Google Scholar 

  49. Mumick, I.S., Pirahesh, H., Ramakrishnan, R.: The magic of duplicates and aggregates. In: Proc. of VLDB, pp. 264–277 (1990)

    Google Scholar 

  50. Nádas, A.: On Turing’s formula for word probabilities. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-33(6), 1414–1416 (1985)

    Article  MATH  Google Scholar 

  51. Ngai, G., Florian, R.: Transformation-based learning in the fast lane. In: Proc. of NAACL-HLT (2001)

    Google Scholar 

  52. van Noord, G., Gerdemann, D.: Finite state transducers with predicates and identities. Grammars 4(3) (2001)

    Google Scholar 

  53. Overton, D.: Precise and Expressive Mode Systems for Typed Logic Programming Languages. Ph.D. thesis, University of Melbourne (2003)

    Google Scholar 

  54. Pelov, N.: Semantics of Logic Programs With Aggregates. Ph.D. thesis, Katholieke Universiteit Leuven (2004)

    Google Scholar 

  55. Ramakrishnan, R., Srivastava, D., Sudarshan, S., Seshadri, P.: The coral deductive system. The VLDB Journal 3(2), 161–210 (1994); Special Issue on Prototypes of Deductive Database Systems

    Article  Google Scholar 

  56. Ramamohanarao, K.: Special issue on prototypes of deductive database systems. VLDB 3(2) (1994)

    Google Scholar 

  57. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107–136 (2006)

    Article  Google Scholar 

  58. Ross, K.A., Sagiv, Y.: Monotonic aggregation in deductive databases. In: Proc. of PODS, pp. 114–126 (1992)

    Google Scholar 

  59. Schmid, H., Rooth, M.: Parse forest computation of expected governors. In: Proc. of ACL (2001)

    Google Scholar 

  60. Shieber, S.M., Schabes, Y., Pereira, F.: Principles and implementation of deductive parsing. Journal of Logic Programming 24(1-2), 3–36 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  61. Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proc. of AAAI, pp. 1094–1099. AAAI Press, Menlo Park (2008)

    Google Scholar 

  62. Van Emden, M.H., Kowalski, R.A.: The semantics of predicate logic as a programming language. JACM 23(4), 733–742 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  63. Van Gelder, A., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. Journal of the ACM 38(3), 620–650 (1991)

    MathSciNet  MATH  Google Scholar 

  64. Williams, R., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1(2), 270–280 (1989)

    Article  Google Scholar 

  65. XSB, http://xsb.sourceforge.net/

  66. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. In: Exploring Artificial Intelligence in the New Millennium, ch. 8. Science & Technology Books (2003)

    Google Scholar 

  67. Younger, D.H.: Recognition and parsing of context-free languages in time n 3. Information and Control 10(2), 189–208 (1967)

    Article  MATH  Google Scholar 

  68. Zhang, M., Zhang, H., Li, H.: Convolution kernel over packed parse forest. In: Proc. of ACL, pp. 875–885 (2010)

    Google Scholar 

  69. Zhu, S.C., Mumford, D.: A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision 2(4), 259–362 (2006)

    Article  MATH  Google Scholar 

  70. Zukowski, U., Freitag, B.: The deductive database system LOLA. In: Fuhrbach, U., Dix, J., Nerode, A. (eds.) LPNMR 1997. LNCS (LNAI), vol. 1265, pp. 375–386. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Johns Hopkins University, 3400 N. Charles Ave., Baltimore, MD, 21218, USA

    Jason Eisner & Nathaniel W. Filardo

Authors
  1. Jason Eisner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathaniel W. Filardo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Oxford University, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Oege de Moor

  2. Department of Computer Science, University of Oxford, UK

    Georg Gottlob , Tim Furche  & Andrew Sellers ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Eisner, J., Filardo, N.W. (2011). Dyna: Extending Datalog for Modern AI. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds) Datalog Reloaded. Datalog 2.0 2010. Lecture Notes in Computer Science, vol 6702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24206-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24206-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24205-2

  • Online ISBN: 978-3-642-24206-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation