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Answer Set Programming for the Semantic Web

(Tutorial)

  • Conference paper
Logic Programming (ICLP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 4670))

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Abstract

The Semantic Web [1,2,3] aims at extending the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and automation of tasks. Its development proceeds in layers, and the Ontology layer is the highest one that has currently reached a sufficient maturity, in the form of the OWL Web Ontology Language (OWL) [4,5], which is based on Description Logics. Current efforts are focused on realizing the Rules layer, which should complement the Ontology layer and offer sophisticated representation and reasoning capabilities. This raises, in particular, the issue of interlinking rules and ontologies. Excellent surveys that classify many proposals for combining rules and ontologies are [6,7]; general issues that arise in this are discussed e.g. in [8,9,10]. Notably, the World Wide Web Consortium (W3C) has installed The Rule Interchange Format (RIF) Working Group on order to produce a core rule language plus extensions which together allow rules to be translated between rule languages and thus transferred between rule systems; a first working draft has been released recently.

Answer Set Programming (ASP) [11,12,13,14], also called A-Prolog [15,16,17], is a well-known declarative programming paradigm which has its roots in Logic Programming and Non-monotonic Reasoning [18]. Thanks to its many extensions [19], ASP is well-suited for modeling and solving problems which involve common sense reasoning, and has been fruitfully applied to a range of applications including data integration, configuration, diagnosis, text mining, reasoning about actions and change, etc.; see [16,17,20].

Within the context of the Semantic Web, the usage of ASP and related formalisms has been explored in different directions:

  • On the one hand, they have been exploited as a tool to encode reasoning tasks in Description Logics, like [16,22,23,24,25,26,27].

  • On the other hand, they have been used as a basis for giving a semantics to a combination of rules and ontologies. Here, increasing levels of integration have been considered:

    • loose couplings, where rule and ontology predicates are separated, and the interaction is via a safe semantic interface like an inference relation e.g. [28,29,30,31,32]

    • tight couplings, where rule and ontology predicates are separated, and the interaction is at the level of models, e.g. [33,34,35,36,37,38,39,10,40]; and

    • full integration, where no distinction between rule and ontology predicates is made, e.g., [41,42,43,44].

In this tutorial, we will first briefly review ASP and ontology formalisms. We then will recall some of the issues that come up with the integration of rules and ontologies. After that, we will consider approaches to combine rules and ontologies under ASP, where particular attention well be devoted to non-monotonic description logic programs [45] and its derivatives [28,46] as a representative of loose couplings. However, also other approaches will be discussed. We further discuss the potential of such combinations, some applications, and finally some open issues.

This tutorial is based on material and results which has been obtained in joint work with Giovambattista Ianni (Università della Calabria), Thomas Krennwallner (TU Wien), Thomas Lukasiewicz (Università di Roma “La Sapienza”), Axel Polleres (DERI Galway), Roman Schindlauer (TU Wien), and Hans Tompits (TU Wien).

The work has been partially supported by the EC NoE REWERSE (IST 506779) and the Austrian Science Fund (FWF) project P17212-N04.

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References

  1. Berners-Lee, T.: Weaving the Web. Harper, San Francisco, CA (1999)

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284, 34–43 (2001)

    Article  Google Scholar 

  3. Fensel, D., Wahlster, W., Lieberman, H., Hendler, J. (eds.): Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, Cambridge (2002)

    Google Scholar 

  4. W3C: OWL Web ontology language overview (2004), Available at http://www.w3.org/TR/2004/~REC-owl-features-20040210/

  5. Horrocks, I., Patel-Schneider, P.F., van Harmelen, F.: From \(\mathcal{SHIQ}\) and RDF to OWL: The making of a Web ontology language. J. Web Sem. 1, 7–26 (2003)

    Google Scholar 

  6. Antoniou, G., Damásio, C.V., Grosof, B., Horrocks, I., Kifer, M., Maluszynski, J., Patel-Schneider, P.F.: Combining rules and ontologies: A survey. Technical Report IST506779/Linköping/I3-D3/D/PU/a1, Linköping University (2005)

    Google Scholar 

  7. Pan, J.Z., Franconi, E., Tessaris, S., Stamou, G., Tzouvaras, V., Serafini, L., Horrocks, I., Glimm, B.: Specification of coordination of rule and ontology languages. Project Deliverable D2.5.1, KnowledgeWeb NoE (2004)

    Google Scholar 

  8. de Bruijn, J, Eiter, T., Polleres, A., Tompits, H.: On representational issues about combinations of classical theories with nonmonotonic rules. In: Lang, J., Lin, F., Wang, J. (eds.) KSEM 2006. LNCS (LNAI), vol. 4092, pp. 1–22. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Eiter, T., Ianni, G., Polleres, A., Schindlauer, R., Tompits, H.: Reasoning with rules and ontologies. In: Barahona, P., Bry, F., Franconi, E., Henze, N., Sattler, U. (eds.) Reasoning Web. LNCS, vol. 4126, pp. 93–127. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Rosati, R.: Integrating ontologies and rules: Semantic and computational issues. In: Barahona, P., Bry, F., Franconi, E., Henze, N., Sattler, U. (eds.) Reasoning Web. LNCS, vol. 4126, pp. 128–151. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Provetti, A., Cao, S.T. (eds.): Proc. AAAI 2001 Spring Symposium on Answer Set Programming: Towards Efficient and Scalable Knowledge Representation and Reasoning. AAAI Press, Stanford (2001)

    Google Scholar 

  12. Lifschitz, V.: Answer Set Programming and Plan Generation (Seminal paper at ICLP 1999 (invited talk), coining the term Answer Set Programming). Artificial Intelligence 138, 39–54 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  13. Marek, V.W., Truszczyński, M.: Stable Models and an Alternative Logic Programming Paradigm. In: Apt, K., Marek, V.W., Truszczyński, M., Warren, D.S. (eds.) The Logic Programming Paradigm – A 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)

    Google Scholar 

  14. Niemelä, I.: Logic Programming with Stable Model Semantics as Constraint Programming Paradigm. Ann. Math. and Artif. Int. 25, 241–273 (1999)

    Article  MATH  Google Scholar 

  15. Balduccini, M., Gelfond, M.: Diagnostic reasoning with A-Prolog. Theory and Practice of Logic Programming 3, 425–461 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  16. Baral, C.: Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press, Cambridge (2002)

    Google Scholar 

  17. Gelfond, M.: Representing knowledge in A-Prolog. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 413–451. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385 (1991)

    Article  Google Scholar 

  19. Niemelä, I. (ed.): Language extensions and software engineering for ASP. Tech. Rep. WP3, Working Group on Answer Set Programming (WASP, IST-FET-2001-37004) (2005), http://www.tcs.hut.fi/Research/Logic/wasp/wp3/wasp-wp3-web/

  20. Woltran, S.: Answer set programming: Model applications and proofs-of-concept. Tech. Rep. WP5, Working Group on Answer Set Programming (WASP, IST-FET-2001-37004) (2005), http://www.kr.tuwien.ac.at/projects/WASP/report.html

  21. Bertino, E., Provetti, A., Salvetti, F.: Local closed-world assumptions for reasoning about semantic web data. In: Buccafurri, F. (ed.) Proc. APPIA-GULP-PRODE, pp. 314–323 (2003)

    Google Scholar 

  22. Van Belleghem, K., Denecker, M., De Schreye, D.: A strong correspondence between description logics and open logic programming. In: Proc. ICLP 1997, pp. 346–360. MIT Press, Cambridge (1997)

    Google Scholar 

  23. Alsaç, G., Baral, C.: Reasoning in description logics using declarative logic programming. Tech. Rep. CS&E Dept, Arizona State University (2001)

    Google Scholar 

  24. Swift, T.: Deduction in ontologies via ASP. In: Lifschitz, V., Niemelä, I. (eds.) Logic Programming and Nonmonotonic Reasoning. LNCS (LNAI), vol. 2923, pp. 275–288. Springer, Heidelberg (2003)

    Google Scholar 

  25. Hustadt, U., Motik, B., Sattler, U.: Reducing SHIQ-description logic to disjunctive datalog programs. In: Proc. KR 2004, pp. 152–162. AAAI Press, Stanford (2004)

    Google Scholar 

  26. Heymans, S., Vermeir, D.: Integrating ontology languages and answer set programming. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 584–588. Springer, Heidelberg (2003)

    Google Scholar 

  27. Heymans, S., Vermeir, D.: Integrating semantic web reasoning and answer set programming. In: Proc. ASP 2003, pp. 194–208 (2003)

    Google Scholar 

  28. Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: Proc. IJCAI 2005, Professional Book Center, pp. 90–96 (2005)

    Google Scholar 

  29. Lukasiewicz, T.: Probabilistic description logic programs. In: Godo, L. (ed.) ECSQARU 2005. LNCS (LNAI), vol. 3571, pp. 737–749. Springer, Heidelberg (2005)

    Google Scholar 

  30. Lukasiewicz, T.: Fuzzy description logic programs under the answer set semantics for the Semantic Web. In: Proc. RuleML 2006, pp. 89–96. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  31. Wang, K., Antoniou, G., Topor, R.W., Sattar, A.: Merging and aligning ontologies in dl-programs. In: Adi, A., Stoutenburg, S., Tabet, S. (eds.) RuleML 2005. LNCS, vol. 3791, pp. 160–171. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  32. Yang, F., Chen, X., Wang, Z.: p-dl-programs: Combining dl-programs with preference for Semantic Web. (Manuscript, 2006)

    Google Scholar 

  33. Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: Combining logic programs with description logics. In: Proc. WWW 2003, pp. 48–57. ACM Press, New York (2003)

    Chapter  Google Scholar 

  34. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web rule language combining OWL and RuleML, W3C Member Submission. (2004), http://www.w3.org/Submission/SWRL/

  35. Donini, F.M., Lenzerini, M., Nardi, D., Schaerf, A.: AL-log: Integrating datalog and description logics. J. Intell. Inf. Syst. 10, 227–252 (1998)

    Google Scholar 

  36. Levy, A.Y., Rousset, M.C.: Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  37. Rosati, R.: Towards expressive KR systems integrating datalog and description logics: Preliminary report. In: Proc. DL 1999, pp. 160–164 (1999)

    Google Scholar 

  38. Rosati, R.: On the decidability and complexity of integrating ontologies and rules. J. Web Sem. 3, 61–73 (2005)

    MathSciNet  Google Scholar 

  39. Rosati, R.: DL log: Tight integration of description logics and disjunctive datalog. In: Proc. KR 2006, pp. 68–78. AAAI Press, Stanford (2006)

    Google Scholar 

  40. Yang, F., Chen, X.: DLclog: A hybrid system integrating rules and description logics with circumscription. In: Proc. DL 2007 (2007), http://www.inf.unibz.it/krdb/events/dl-2007/

  41. Motik, B., Horrocks, I., Rosati, R., Sattler, U.: Can OWL and logic programming live together happily ever after? In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 501–514. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  42. de Bruijn, J., Pearce, D., Polleres, A., Valverde, A.: A logic for hybrid rules. In: Proc. RuleML 2006, IEEE Computer Society Press, Washington (2006), http://2006.ruleml.org/online-proceedings/rule-integ.pdf

    Google Scholar 

  43. de Bruijn, J., Pearce, D., Polleres, A., Valverde, A.: Quantified equilibrium logic and hybrid rules. In: Marchiori, M., Pan, S.E., de Sainte Marie, C. (eds.) RR 2007. LNCS, vol. 4524, Springer, Heidelberg (2007)

    Google Scholar 

  44. Motik, B., Rosati, R.: A faithful integration of description logics with logic programming. In: Proc. IJCAI 2007, pp. 477–482. AAAI Press, Stanford (2007)

    Google Scholar 

  45. Eiter, T., Lukasiewicz, T., Schindlauer, R., Tompits, H.: Combining Answer Set Programming with Description Logics for the Semantic Web. In: Proc. KR 2004, pp. 141–151 (2004)

    Google Scholar 

  46. Eiter, T., Ianni, G., Krennwallner, T., Schindlauer, R.: Exploiting conjunctive queries in description logic programs. In: Proc. DL 2007 (2007), http://www.inf.unibz.it/krdb/events/dl-2007/

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  1. Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria

    Thomas Eiter

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Véronica Dahl Ilkka Niemelä

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Eiter, T. (2007). Answer Set Programming for the Semantic Web. In: Dahl, V., Niemelä, I. (eds) Logic Programming. ICLP 2007. Lecture Notes in Computer Science, vol 4670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74610-2_3

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  • DOI: https://doi.org/10.1007/978-3-540-74610-2_3

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