Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Semantics and Inference for Probabilistic Description Logics

Authors : Riccardo Zese, Elena Bellodi, Evelina Lamma, Fabrizio Riguzzi, Fabiano Aguiari

Published in: Uncertainty Reasoning for the Semantic Web III

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We present a semantics for Probabilistic Description Logics that is based on the distribution semantics for Probabilistic Logic Programming. The semantics, called DISPONTE, allows to express assertional probabilistic statements. We also present two systems for computing the probability of queries to probabilistic knowledge bases: BUNDLE and TRILL. BUNDLE is based on the Pellet reasoner while TRILL exploits the declarative Prolog language. Both algorithms compute a propositional Boolean formula that represents the set of explanations to the query. BUNDLE builds a formula in Disjunctive Normal Form in which each disjunct corresponds to an explanation while TRILL computes a general Boolean pinpointing formula using the techniques proposed by Baader and Peñaloza. Both algorithms then build a Binary Decision Diagram (BDD) representing the formula and compute the probability from the BDD using a dynamic programming algorithm. We also present experiments comparing the performance of BUNDLE and TRILL.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Learning Probabilistic Description Logics
next chapter A Metaontology for Annotating Ontology Entities with Vagueness Descriptions
Footnotes
Literature
1.
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, Cambridge (2003)
2.
Baader, F., Horrocks, I., Sattler, U.: Description logics. In: Handbook of Knowledge Representation, chap. 3, pp. 135–179. Elsevier, Amsterdam (2008)
3.
Baader, F., Peñaloza, R.: Automata-based axiom pinpointing. J. Autom. Reasoning 45(2), 91–129 (2010)MATHCrossRef
4.
Baader, F., Peñaloza, R.: Axiom pinpointing in general tableaux. J. Log. Comput. 20(1), 5–34 (2010)MATHCrossRef
5.
Bacchus, F.: Representing and Reasoning with Probabilistic Knowledge - A Logical Approach to Probabilities. MIT Press, Cambridge (1990)
6.
7.
Bellodi, E., Lamma, E., Riguzzi, F., Albani, S.: A distribution semantics for probabilistic ontologies. In: International Workshop on Uncertainty Reasoning for the Semantic Web. CEUR Workshop Proceedings, vol. 778. Sun SITE Central Europe (2011)
8.
Darwiche, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. 17, 229–264 (2002)MATHMathSciNet
9.
De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: a probabilistic Prolog and its application in link discovery. In: International Joint Conference on Artificial Intelligence, pp. 2462–2467 (2007)
10.
Faizi, I.: A description logic prover in prolog, Bachelor’s thesis, Informatics Mathematical Modelling, Technical University of Denmark (2011)
11.
Gomes, C.P., Sabharwal, A., Selman, B.: Model counting. In: Biere, A. (ed.) Handbook of Satisfiability. IOS Press, Amsterdam (2008)
12.
Haarslev, V., Hidde, K., Möller, R., Wessel, M.: The racerpro knowledge representation and reasoning system. Semant. Web 3(3), 267–277 (2012)
13.
Halaschek-Wiener, C., Kalyanpur, A., Parsia, B.: Extending tableau tracing for ABox updates. Technical report, University of Maryland (2006)
14.
Halpern, J.Y.: An analysis of first-order logics of probability. Artif. Intell. 46(3), 311–350 (1990)MATHCrossRef
15.
Herchenröder, T.: Lightweight semantic web oriented reasoning in prolog: tableaux inference for description logics. Master’s thesis, School of Informatics, University of Edinburgh (2006)
16.
Hitzler, P., Krötzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies. CRC Press, Boca Raton (2009)
17.
Hustadt, U., Motik, B., Sattler, U.: Deciding expressive description logics in the framework of resolution. Inf. Comput. 206(5), 579–601 (2008)MATHMathSciNetCrossRef
18.
Kalyanpur, A.: Debugging and repair of OWL ontologies. Ph.D. thesis, The Graduate School of the University of Maryland (2006)
19.
Kalyanpur, A., Parsia, B., Horridge, M., Sirin, E.: Finding all justifications of OWL DL entailments. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 267–280. Springer, Heidelberg (2007)CrossRef
20.
Kalyanpur, A., Parsia, B., Sirin, E., Hendler, J.A.: Debugging unsatisfiable classes in OWL ontologies. J. Web Sem. 3(4), 268–293 (2005)CrossRef
21.
Klinov, P.: Pronto: a non-monotonic probabilistic description logic reasoner. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 822–826. Springer, Heidelberg (2008)CrossRef
22.
Klinov, P., Parsia, B.: Optimization and evaluation of reasoning in probabilistic description logic: towards a systematic approach. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 213–228. Springer, Heidelberg (2008)CrossRef
23.
Klinov, P., Parsia, B.: A hybrid method for probabilistic satisfiability. In: Bjørner, N., Sofronie-Stokkermans, V. (eds.) CADE 2011. LNCS, vol. 6803, pp. 354–368. Springer, Heidelberg (2011)CrossRef
24.
Lukácsy, G., Szeredi, P.: Efficient description logic reasoning in prolog: the dlog system. TPLP 9(3), 343–414 (2009)MATH
25.
26.
Meissner, A.: An automated deduction system for description logic with alcn language. Studia z Automatyki i Informatyki 28–29, 91–110 (2004)
27.
28.
Patel-Schneider, P.F., Horrocks, I., Bechhofer, S.: Tutorial on OWL (2003)
29.
30.
Poole, D.: Probabilistic horn abduction and Bayesian networks. Artif. Intell. 64(1), 81–129 (1993)MATHCrossRef
31.
32.
Ricca, F., Gallucci, L., Schindlauer, R., Dell’Armi, T., Grasso, G., Leone, N.: Ontodlv: an asp-based system for enterprise ontologies. J. Log. Comput. 19(4), 643–670 (2009)MATHMathSciNetCrossRef
33.
34.
Riguzzi, F., Bellodi, E., Lamma, E.: Probabilistic Datalog+/- under the distribution semantics. In: Kazakov, Y., Lembo, D., Wolter, F. (eds.) International Workshop on Description Logics (2012)
35.
Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: Computing instantiated explanations in OWL DL. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 397–408. Springer, Heidelberg (2013)CrossRef
36.
Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: Probabilistic description logics under the distribution semantics. Semant. Web J. (to appear, 2014)
37.
Riguzzi, F., Lamma, E., Bellodi, E., Zese, R.: Epistemic and statistical probabilistic ontologies. In: Uncertainty Reasoning for the Semantic Web. CEUR Workshop Proceedings, vol. 900, pp. 3–14. Sun SITE Central Europe (2012)
38.
Sang, T., Beame, P., Kautz, H.A.: Performing bayesian inference by weighted model counting. In: Proceedings of AAAI, pp. 475–482. AAAI Press/The MIT Press, Palo Alto, Pittsburgh, 9–13 July 2005
39.
Sato, T.: A statistical learning method for logic programs with distribution semantics. In: International Conference on Logic Programming, pp. 715–729. MIT Press (1995)
40.
Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. J. Artif. Intell. Res. 15, 391–454 (2001)MATHMathSciNet
41.
Schlobach, S., Cornet, R.: Non-standard reasoning services for the debugging of description logic terminologies. In: International Joint Conference on Artificial Intelligence, pp. 355–362. Morgan Kaufmann (2003)
42.
Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Artif. Intell. 48(1), 1–26 (1991)MATHCrossRef
43.
Shearer, R., Motik, B., Horrocks, I.: Hermit: A highly-efficient owl reasoner. In: OWLED (2008)
44.
Sirin, E., Parsia, B., Cuenca-Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. J. Web Sem. 5(2), 51–53 (2007)CrossRef
45.
Vassiliadis, V., Wielemaker, J., Mungall, C.: Processing owl2 ontologies using thea: an application of logic programming. In: International Workshop on OWL: Experiences and Directions. CEUR Workshop Proceedings, vol. 529. CEUR-WS.org (2009)
46.
Vennekens, J., Denecker, M., Bruynooghe, M.: CP-logic: a language of causal probabilistic events and its relation to logic programming. Theory Pract. Log. Program. 9(3), 245–308 (2009)MATHMathSciNetCrossRef
47.
Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic programs with annotated disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431–445. Springer, Heidelberg (2004)CrossRef
Metadata
Title
Semantics and Inference for Probabilistic Description Logics
Authors
Riccardo Zese
Elena Bellodi
Evelina Lamma
Fabrizio Riguzzi
Fabiano Aguiari
Copyright Year
2014
Book
Uncertainty Reasoning for the Semantic Web III

Print ISBN: 978-3-319-13412-3

Electronic ISBN: 978-3-319-13413-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-13413-0_5

Premium Partner

    Image Credits