Skip to main content
SpringerLink
Log in

NoHR: An Overview

Reasoning with Ontologies and Nonmonotonic Rules

  • Systems Description
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Description logic ontologies, such as ontologies written in OWL, and non-monotonic rules, as known in Logic Programming, are two major approaches in Knowledge Representation and Reasoning. Even though their integration is challenging due to their inherent differences, the need to combine their distinctive features stems from real world applications. In this paper, we give an overview of NoHR, a reasoner designed to answer queries over theories composed of an OWL ontology in a Description logic and a set of non-monotonic rules. NoHR has been developed as a plug-in for the widely used ontology editor Protégé, building on a combination of reasoners dedicated to OWL and rules, but it is also available as a library, allowing for its integration within other environments and applications. It comes with support for all polynomial OWL profiles and the integration of their constructors as well as for standard built-in Prolog predicates, and allows the direct consultation of databases during query evaluation and the usage of sophisticated mechanisms, such as tabling already computed results, all of which enhances the applicability and the efficiency of query answering.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. http://www.w3.org.

  2. http://www.ihtsdo.org/snomed-ct/.

  3. http://nohr.di.fct.unl.pt.

  4. http://protege.stanford.edu.

  5. http://xsb.sourceforge.net.

  6. The source code can be obtained at https://github.com/NoHRReasoner/NoHR.

  7. We refer to [1] for a more general and thorough introduction to DLs.

  8. https://bioportal.bioontology.org/ontologies/GALEN.

  9. http://swat.cse.lehigh.edu/projects/lubm/.

  10. Conceptually, this allows to simultaneously view certain predicates under the closed world semantics in rules and under the open world semantics in the ontology, and admits the bidirectional flow of information between both the rules and the ontology.

  11. In general, the notion of DL-safety is used in this context which requires that these variables occur in atoms that do themselves not occur in the ontology, but due to the reasoning method employed in NoHR, we can relax that restriction.

  12. Similar concepts have been used before for adding database support to rule systems, such as \(DLV^{DB}\) [27], and in ontology based data access, such as in ontop [28].

  13. https://protege.stanford.edu/.

  14. http://interprolog.com/java-bridge/.

References

  1. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (eds) (2010) The description logic handbook: theory, implementation, and applications, 3rd edn. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  2. Baral C, Gelfond M (1994) Logic programming and knowledge representation. J Log Program 19(20):73–148

    MathSciNet  MATH  Google Scholar 

  3. Hitzler P, Krötzsch M, Parsia B, Patel-Schneider PF, Rudolph S (2012) OWL 2 web ontology language: primer. W3C, Cambridge

    Google Scholar 

  4. Kifer M, Boley H (eds.): RIF overview (Second Edition). W3C Working Group Note (2013) Available at http://www.w3.org/TR/rif-overview/. Accessed 5 Feb 2013

  5. Motik B, Cuenca Grau B, Horrocks I, Wu Z, Fokoue A, Lutz C (eds) (2012) OWL 2 web ontology language: profiles, 2nd edn. W3C, Cambridge

    Google Scholar 

  6. Eiter T, Ianni G, Lukasiewicz T, Schindlauer R, Tompits H (2008) Combining answer set programming with description logics for the semantic web. Artif Intell 172(12–13):1495–1539

    MathSciNet  MATH  Google Scholar 

  7. Motik B, Rosati R (2010) Reconciling description logics and rules. J ACM 57(5):93–154

    MathSciNet  MATH  Google Scholar 

  8. Eiter T, Simkus M (2015) Linking open-world knowledge bases using nonmonotonic rules. In: Ianni G, Truszczynski M, Calimeri F (eds) Procs. of LPNMR, LNCS, vol 9345. Springer, Berlin, pp 294–308

    Google Scholar 

  9. Patel C et al (2007) Matching patient records to clinical trials using ontologies. In: Aberer K et al (eds) Semantic web. ISWC 2007, ASWC 2007. Lecture notes in computer science, vol 4825. Springer, Berlin, pp 816–829

    Google Scholar 

  10. Alberti M, Knorr M, Gomes AS, Leite J, Gonçalves R, Slota M (2012) Normative systems require hybrid knowledge bases. In: van der Hoek W, Padgham L, Conitzer V, Winikoff M et al (eds) Procs. of AAMAS. IFAAMAS, Montreal pp, pp 1425–1426

    Google Scholar 

  11. Slota M, Leite J, Swift T (2015) On updates of hybrid knowledge bases composed of ontologies and rules. Artif Intell 229:33–104

    MathSciNet  MATH  Google Scholar 

  12. Lopes C, Knorr M, Leite J (2017) Nohr: Integrating XSB prolog with the OWL 2 profiles and beyond. Procs of LPNMR, LNCS, vol 10377. Springer, Berlin, pp 236–249

    Google Scholar 

  13. Kasalica V, Gerochristos I, Alferes JJ, Gomes AS, Knorr M, Leite J (2019) Telco network inventory validation with nohr. In: Lierler Y, Woltran S, Balduccini M (eds) Procs. of LPNMR, LNCS, vol 11481. Springer, Berlin, pp 18–31

    Google Scholar 

  14. Baader F, Brandt S, Lutz C (2005) Pushing the \(\cal{EL}\) envelope. In: Kaelbling LP, Saffiotti A (eds) Procs. of IJCAI. Professional Book Center, pp. 364–369

  15. Knorr M, Alferes JJ, Hitzler P (2011) Local closed world reasoning with description logics under the well-founded semantics. Artif Intell 175(9–10):1528–1554

    MathSciNet  MATH  Google Scholar 

  16. Lifschitz V (1991) Nonmonotonic databases and epistemic queries. In: Mylopoulos J, Reiter R (eds) Procs. of IJCAI. Morgan Kaufmann, Burlington

    Google Scholar 

  17. Gelder AV, Ross KA, Schlipf JS (1991) The well-founded semantics for general logic programs. J ACM 38(3):620–650

    MathSciNet  MATH  Google Scholar 

  18. Alferes JJ, Knorr M, Swift T (2013) Query-driven procedures for hybrid MKNF knowledge bases. ACM Trans Comput Log 14(2):1–43

    MathSciNet  MATH  Google Scholar 

  19. Kazakov Y, Krötzsch M, Simančík F (2013) The incredible ELK: from polynomial procedures to efficient reasoning with \(\cal{EL}\) ontologies. J Autom Reason 53:1–61

    MathSciNet  MATH  Google Scholar 

  20. Glimm B, Horrocks I, Motik B, Stoilos G, Wang Z (2014) Hermit: an OWL 2 reasoner. J Autom Reason 53(3):245–269

    MATH  Google Scholar 

  21. Steigmiller A, Liebig T, Glimm B (2014) Konclude: system description. J Web Sem 27:78–85

    MATH  Google Scholar 

  22. Kaminski T, Knorr M, Leite J (2015) Efficient paraconsistent reasoning with ontologies and rules. In: Yang Q, Wooldridge MJ (eds) Procs. of IJCAI. AAAI Press, Louisiana, pp 3098–3105

    Google Scholar 

  23. Horrocks I, Kutz O, Sattler U (2006) The even more irresistible \(\cal{SROIQ}\). In: Doherty P, Mylopoulos J, Welty CA (eds) Procs. of KR. AAAI Press, Louisiana, pp 57–67

    Google Scholar 

  24. Artale A, Calvanese D, Kontchakov R, Zakharyaschev M (2009) The \(DL\)-\(Lite\) family and relations. J Artif Intell Res (JAIR) 36:1–69

    MathSciNet  MATH  Google Scholar 

  25. Grosof BN, Horrocks I, Volz R, Decker S (2003) Description logic programs: combining logic programs with description logic. In: Hencsey G, White B, Chen YR, Kovács L, Lawrence S (eds) Procs. of WWW. ACM, New York, pp 48–57

    Google Scholar 

  26. Chen W, Warren DS (1996) Tabled evaluation with delaying for general logic programs. J ACM 43(1):20–74

    MathSciNet  MATH  Google Scholar 

  27. Terracina G, Leone N, Lio V, Panetta C (2008) Experimenting with recursive queries in database and logic programming systems. TPLP 8(2):129–165

    MathSciNet  MATH  Google Scholar 

  28. Calvanese D, Cogrel B, Komla-Ebri S, Kontchakov R, Lanti D, Rezk M, Rodriguez-Muro M, Xiao G (2017) Ontop: answering SPARQL queries over relational databases. Semantic Web 8(3):471–487

    Google Scholar 

  29. Ivanov V, Knorr M, Leite J (2013) A query tool for \(\cal{EL}\) with non-monotonic rules. In: Alani H et al (eds) The semantic web—ISWC 2013. ISWC 2013. Lecture notes in computer science, vol 8218. Springer, Berlin, pp 216–231

    Google Scholar 

  30. Costa N, Knorr M, Leite J (2015) Next step for NoHR: OWL 2 QL. In: Arenas M, Corcho Ó, Simperl E, Strohmaier M, d’Aquin M, Srinivas K, Groth PT, Dumontier M, Heflin J, Thirunarayan K, Staab S (eds.) Procs. of ISWC, LNCS, vol. 9366, pp. 569–586

  31. Eiter T, Fink M, Ianni G, Krennwallner T, Redl C, Schüller P (2016) A model building framework for answer set programming with external computations. TPLP 16(4):418–464

    MathSciNet  MATH  Google Scholar 

  32. Eiter T, Germano S, Ianni G, Kaminski T, Redl C, Schüller P, Weinzierl A (2018) The DLVHEX system. KI 32(2–3):187–189

    Google Scholar 

  33. Redl C (2019) Inlining external sources in answer set programs. TPLP 19(3):360–411

    MathSciNet  Google Scholar 

  34. Bajraktari L, Ortiz M, Simkus M (2018) Combining rules and ontologies into clopen knowledge bases. In: McIlraith SA, Weinberger KQ (eds) Procs. of AAAI. AAAI Press, Louisiana, pp 1728–1735

    Google Scholar 

  35. Knorr M, Hitzler P, Maier F (2012) Reconciling OWL and non-monotonic rules for the semantic web. In: Raedt LD, Bessiere C, Dubois D, Doherty P, Frasconi P, Heintz F, Lucas PJF (eds) Procs. of ECAI, frontiers in artificial intelligence and applications, vol 242. IOS Press, Amsterdam, pp 474–479

    MATH  Google Scholar 

  36. Glimm B, Stuckenschmidt H (2016) 15 years of semantic web: an incomplete survey. KI 30(2):117–130

    Google Scholar 

  37. Latif A, Scherp A, Tochtermann K (2016) LOD for library science: benefits of applying linked open data in the digital library setting - retrospects and research topics. KI 30(2):149–157

    Google Scholar 

  38. Zapilko B, Schaible J, Wandhöfer T, Mutschke P (2016) Applying linked data technologies in the social sciences. KI 30(2):159–162

    Google Scholar 

  39. Slota M, Leite J (2010) Towards closed world reasoning in dynamic open worlds. TPLP 10(4–6):547–563

    MathSciNet  MATH  Google Scholar 

  40. Slota M, Leite J, Swift T (2011) Splitting and updating hybrid knowledge bases. TPLP 11(4–5):801–819

    MathSciNet  MATH  Google Scholar 

  41. Slota M, Leite J (2012) A unifying perspective on knowledge updates. In: del Cerro LF, Herzig A, Mengin J (eds) Procs of JELIA, LNCS, vol 7519. Springer, Berlin, pp 372–384

    Google Scholar 

  42. Slota M, Leite J (2014) The rise and fall of semantic rule updates based on se-models. TPLP 14(6):869–907

    MathSciNet  MATH  Google Scholar 

  43. Beck H, Dao-Tran M, Eiter T (2018) LARS: a logic-based framework for analytic reasoning over streams. Artif Intell 261:16–70

    MathSciNet  MATH  Google Scholar 

  44. Beck H, Dao-Tran M, Eiter T, Folie C (2018) Stream reasoning with LARS. KI 32(2–3):193–195

    Google Scholar 

  45. Brewka G, Ellmauthaler S, Gonçalves R, Knorr M, Leite J, Pührer J (2018) Reactive multi-context systems: heterogeneous reasoning in dynamic environments. Artif Intell 256:68–104

    MathSciNet  MATH  Google Scholar 

  46. Brewka G, Ellmauthaler S, Pührer J (2014) Multi-context systems for reactive reasoning in dynamic environments. In: Schaub T, Friedrich G, O’Sullivan B (eds) Procs of ECAI, frontiers in artificial intelligence and applications, vol 263. IOS Press, Amsterdam, pp 159–164

    MATH  Google Scholar 

  47. Gonçalves R, Knorr M, Leite J (2014) Evolving multi-context systems. In: Friedrich G, O’Sullivan B (eds) Procs. of ECAI, frontiers in artificial intelligence and applications, vol 263. IOS Press, Amsterdam, pp 375–380

    Google Scholar 

  48. Knorr M, Slota M, Leite J, Homola M (2014) What if no hybrid reasoner is available? hybrid MKNF in multi-context systems. J Log Comput 24(6):1279–1311

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for the helpful comments and thank Nuno Costa and Vadim Ivanov for their contributions to the development of NoHR.

Author information

Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

    Vedran Kasalica

  2. Departamento de Informática, NOVA LINCS, FCT-NOVA Lisboa, 2829-516, Caparica, Portugal

    Matthias Knorr & João Leite

  3. Centre of Technology and Systems, UNINOVA, FCT-NOVA Lisboa, 2829-516, Caparica, Portugal

    Carlos Lopes

Authors
  1. Vedran Kasalica
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthias Knorr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Leite
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Knorr.

Additional information

Partially supported by FCT projects RIVER (PTDC/CCI-COM/30952/2017) and NOVA LINCS (UIDB/04516/2020).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kasalica, V., Knorr, M., Leite, J. et al. NoHR: An Overview. Künstl Intell 34, 509–515 (2020). https://doi.org/10.1007/s13218-020-00650-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-020-00650-1

Keywords

Search

Navigation