Skip to main content
SpringerLink
Log in

Probabilistic Logic and Relational Models

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Social Network Analysis and Mining

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 1,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  • Blockeel H, De Raedt L (1998) Top-down induction of first-order logical decision trees. Artif Intell 101(1–2):285–297

    MATH  MathSciNet  Google Scholar 

  • Breese JS (1992) Construction of belief and decision networks. Comput Intell 8(4):624–647

    Google Scholar 

  • Breese JS, Goldman RP, Wellman MP (1994) Introduction to the special section on knowledge-based construction of probabilistic decision models. IEEE Trans Syst Man Cybern 24(11):1577

    Google Scholar 

  • Chavira M, Darwiche A (2008) On probabilistic inference by weighted model counting. Artif Intell 172:772–799

    MATH  MathSciNet  Google Scholar 

  • de Salvo Braz R, Amir E, Roth D (2005) Lifted first-order probabilistic inference. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI-05), Edinburgh

    Google Scholar 

  • Fierens D, den Broeck GV, Thon I, Gutmann B, De Raedt L (2011) Inference in probabilistic logic programs using weighted CNF’s. In: Proceedings of the 27th conference on uncertainty in artificial intelligence (UAI-11), Barcelona

    Google Scholar 

  • Friedman N, Getoor L, Koller D, Pfeffer A (1999) Learning probabilistic relational models. In: Proceedings of the 16th international joint conference on artificial intelligence (IJCAI-99), Stockholm

    Google Scholar 

  • Gilks WR, Thomas A, Spiegelhalter DJ (1994) A language and program for complex Bayesian modelling. Statistician 43(1):169–177

    Google Scholar 

  • Gogate V, Domingos P (2011) Probabilistic theorem proving. In: Proceedings of the 27th conference of uncertainty in artificial intelligence (UAI-11), Barcelona

    Google Scholar 

  • Goodman ND, Mansinghka VK, Roy D, Bonawitz K, Tenenbaum JB (2008) Church: a language for generative models. In: Proceedings of the 24th conference on uncertainty in artificial intelligence (UAI-08), Helsinki

    Google Scholar 

  • Haddawy P (1994) Generating Bayesian networks from probability logic knowledge bases. In: Proceedings of the 10th conference on uncertainty in artificial intelligence (UAI-94), Seattle

    Google Scholar 

  • Halpern J (1990) An analysis of first-order logics of probability. Artif Intell 46:311–350

    MATH  Google Scholar 

  • Heckerman D, Meek C, Koller D (2007) Probabilistic entity-relationship models, PRMs, and plate models. In: Getoor L, Taskar B (eds) Introduction to statistical relational learning. MIT, Cambridge

    Google Scholar 

  • Jaeger M (1997) Relational Bayesian networks. In: Proceedings of the 13th conference of uncertainty in artificial intelligence (UAI-97), Providence

    Google Scholar 

  • Jaeger M (2000) On the complexity of inference about probabilistic relational models. Artif Intell 117:297–308

    MATH  MathSciNet  Google Scholar 

  • Kersting K, De Raedt L (2001) Towards combining inductive logic programming and Bayesian networks. In: Proceedings of the eleventh international conference on inductive logic programming (ILP-2001), Strasbourg. Lecture notes in artificial intelligence, vol 2157. Springer, Berlin

    Google Scholar 

  • Kimmig A, Demoen B, De Raedt L, Santos Costa V, Rocha R (2011) On the implementation of the probabilistic logic programming language problog. Theory Pract Logic Program 11(2–3):235–262

    MATH  MathSciNet  Google Scholar 

  • Laskey KB (2008) MEBN: a language for first-order Bayesian knowledge bases. Artif Intell 172(2–3):140–178

    MATH  MathSciNet  Google Scholar 

  • Laskey KB, Mahoney SM (1997) Network fragments: representing knowledge for constructing probabilistic models. In: Proceedings of the 13th annual conference on uncertainty in artificial intelligence (UAI-97), Providence

    Google Scholar 

  • Milch B, Marthi B, Russell S, Sontag D, Ong D, Kolobov A (2005) BLOG: probabilistic logic with unknown objects. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI-05), Edinburgh

    Google Scholar 

  • Milch B, Zettlemoyer LS, Kersting K, Haimes M, Kaelbling LP (2008) Lifted probabilistic inference with counting formulas. In: Proceedings of the 23rd AAAI conference on artificial intelligence (AAAI-08), Chicago

    Google Scholar 

  • Muggleton S (1996) Stochastic logic programs. In: De Raedt L (ed) Advances in inductive logic programming. IOS, Amsterdam, pp 254–264

    Google Scholar 

  • Neville J, Jensen D, Friedland L, Hay M (2003) Learning relational probability trees. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-03), Washington, DC

    Google Scholar 

  • Ng KS, Lloyd JW, Uther WTB (2008) Probabilistic modelling, inference and learning using logical theories. Ann Math Artif Intell 54(1–3):159–205

    MATH  MathSciNet  Google Scholar 

  • Ngo L, Haddawy P (1997) Answering queries from context-sensitive probabilistic knowledge bases. Theor Comput Sci 171:147–177

    MATH  MathSciNet  Google Scholar 

  • Nilsson N (1986) Probabilistic logic. Artif Intell 28:71–88

    MATH  MathSciNet  Google Scholar 

  • Pfeffer A (2001) IBAL: a probabilistic rational programming language. In: Proceedings of the 17th international joint conference on artificial intelligence (IJCAI-01), Seattle

    Google Scholar 

  • Poole D (1993) Probabilistic horn abduction and Bayesian networks. Artif Intell 64:81–129

    MATH  Google Scholar 

  • Poole D (2003) First-order probabilistic inference. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI-03), Acapulco

    Google Scholar 

  • Poole D (2008) The independent choice logic and beyond. In: De Raedt L, Frasconi P, Kersting K, Muggleton S (eds) Probabilistic inductive logic programming. Lecture notes in artificial intelligence, vol 4911. Springer, Berlin/New York, pp 222–243

    Google Scholar 

  • Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1–2):107–136

    Google Scholar 

  • Sato T (1995) A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th international conference on logic programming (ICLP’95), Tokyo

    Google Scholar 

  • Van den Broeck G, Taghipour N, Meert W, Davis J, De Raedt L (2011) Lifted probabilistic inference by first-order knowledge compilation. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI-11), Barcelona

    Google Scholar 

Recommended Reading

  • De Raedt L (2008) Logical and relational learning. Springer, Berlin

    MATH  Google Scholar 

  • De Raedt L, Frasconi P, Kersting K, Muggleton S (eds) (2008) Probabilistic inductive logic programming. Lecture notes in artificial intelligence, vol 4911. Springer, Berlin

    Google Scholar 

  • Getoor L, Taskar B (eds) (2007) Introduction to statistical relational learning. MIT, Cambridge

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Manfred Jaeger

Authors
  1. Manfred Jaeger
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Jaeger, M. (2014). Probabilistic Logic and Relational Models. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_157

Download citation

Publish with us

Policies and ethics

Search

Navigation