Answer Set Programming with External Source Access

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10370)


Access to external information is an important need for Answer Set Programming (ASP), which is a booming declarative problem solving approach these days. External access not only includes data in different formats, but more general also the results of computations, and possibly in a two-way information exchange. Providing such access is a major challenge, and in particular if it should be supported at a generic level, both regarding the semantics and efficient computation. In this article, we consider problem solving with ASP under external information access using the dlvhex system. The latter facilitates this access through special external atoms, which are two-way API style interfaces between the rules of the program and an external source. The dlvhex system has a flexible plugin architecture that allows one to use multiple predefined and user-defined external atoms which can be implemented, e.g., in Python or C++. We consider how to solve problems using the ASP paradigm, and specifically discuss how to use external atoms in this context, illustrated by examples. As a showcase, we demonstrate the development of a hex program for a concrete real-world problem using Semantic Web technologies, and discuss specifics of the implementation process.


External Source Logic Program Description Logic Weak Constraint Disjunctive Logic Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We appreciate the review feedback and are thankful for the detailed suggestions in it to improve the presentation of the material in this article. We also thank Roland Kaminski and Torsten Schaub for comments regarding Clingo.


  1. 1.
    Alviano, M., Dodaro, C., Faber, W., Leone, N., Ricca, F.: WASP: a native ASP solver based on constraint learning. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS, vol. 8148, pp. 54–66. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40564-8_6 CrossRefGoogle Scholar
  2. 2.
    Analyti, A., Antoniou, G., Damásio, C.V.: MWeb: a principled framework for modular web rule bases and its semantics. ACM Trans. Comput. Log. 12(2), 17 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Apt, K.: Principles of Constraint Programming. Cambridge University Press, New York (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Balduccini, M.: Representing constraint satisfaction problems in answer set programming. In: Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP) at ICLP (2009)Google Scholar
  5. 5.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Basol, S., Erdem, O., Fink, M., Ianni, G.: HEX programs with action atoms. In: Technical Communications of the International Conference on Logic Programming (ICLP), pp. 24–33 (2010)Google Scholar
  7. 7.
    Ben-Eliyahu, R., Dechter, R.: Propositional semantics for disjunctive logic programs. Ann. Math. Artif. Intell. 12, 53–87 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Bikakis, A., Antoniou, G.: Defeasible contextual reasoning with arguments in ambient intelligence. IEEE Trans. Knowl. Data Eng. 22(11), 1492–1506 (2010)CrossRefGoogle Scholar
  9. 9.
    Bögl, M., Eiter, T., Fink, M., Schüller, P.: The mcs-ie system for explaining inconsistency in multi-context systems. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS (LNAI), vol. 6341, pp. 356–359. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15675-5_31 CrossRefGoogle Scholar
  10. 10.
    Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: Contextualizing ontologies. Web Semant. Sci. Serv. Agents World Wide Web 1(4), 325–343 (2004)CrossRefGoogle Scholar
  11. 11.
    Bozzato, L., Serafini, L.: Materialization calculus for contexts in the semantic web. In: Eiter, T., Glimm, B., Kazakov, Y., Krötzsch, M. (eds.) DL 2013, vol. 1014. CEUR-WP, pp. 552–572 (2013).
  12. 12.
    Brewka, G., Eiter, T.: Equilibria in heterogeneous nonmonotonic multi-context systems. In: AAAI Conference on Artificial Intelligence, pp. 385–390. AAAI Press (2007)Google Scholar
  13. 13.
    Brewka, G., Eiter, T., Truszczyński, M. (eds.): AI Magazine 37(3), 5–6 (2016). Special issue on Answer Set Programming. AAAI PressGoogle Scholar
  14. 14.
    Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  15. 15.
    Brewka, G., Roelofsen, F., Serafini, L.: Contextual default reasoning. In: Veloso, M.M. (ed.) IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6–12 January 2007, pp. 268–273 (2007)Google Scholar
  16. 16.
    Cabalar, P., Kaminski, R., Ostrowski, M., Schaub, T.: An ASP semantics for default reasoning with constraints. In: Kambhampati, S. (ed.) Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, 9–15 July 2016, pp. 1015–1021. IJCAI/AAAI Press (2016)Google Scholar
  17. 17.
    Calì, A., Gottlob, G., Pieris, A.: Towards more expressive ontology languages: the query answering problem. Artif. Intell. 193, 87–128 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Calimeri, F., Cozza, S., Ianni, G.: External sources of knowledge and value invention in logic programming. Ann. Math. Artif. Intell. 50(3–4), 333–361 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F., Schaub, T.: ASP-Core-2 Input Language Format (2013)Google Scholar
  20. 20.
    Calimeri, F., Fink, M., Germano, S., Humenberger, A., Ianni, G., Redl, C., Stepanova, D., Tucci, A., Wimmer, A.: Angry-HEX: an artificial player for angry birds based on declarative knowledge bases. IEEE Trans. Comput. Intell. AI Games 8(2), 128–139 (2016)CrossRefGoogle Scholar
  21. 21.
    Calimeri, F., Fink, M., Germano, S., Ianni, G., Redl, C., Wimmer, A.: AngryHEX: an artificial player for angry birds based on declarative knowledge bases. In: National Workshop and Prize on Popularize, Artificial Intelligence, pp. 29–35 (2013)Google Scholar
  22. 22.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reasoning 39(3), 385–429 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Dantsin, E., Eiter, T., Gottlob, G., Voronkov, A.: Complexity and expressive power of logic programming. ACM Comput. Surv. 33(3), 374–425 (2001)CrossRefGoogle Scholar
  24. 24.
    Dao-Tran, M., Eiter, T., Krennwallner, T.: Realizing default logic over description logic knowledge bases. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 602–613. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02906-6_52 CrossRefGoogle Scholar
  25. 25.
    Dodaro, C., Ricca, F., Schüller,P.: External propagators in WASP: preliminary report. In: Bistarelli, S., Formisano, A., Maratea, M. (eds.) International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), vol. 1745. CEUR Workshop Proceedings, pp. 1–9, November 2016.
  26. 26.
    Drabent, W., Eiter, T., Ianni, G., Krennwallner, T., Lukasiewicz, T., Małuszyński, J.: Hybrid reasoning with rules and ontologies. In: Bry, F., Małuszyński, J. (eds.) Semantic Techniques for the Web. LNCS, vol. 5500, pp. 1–49. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04581-3_1 CrossRefGoogle Scholar
  27. 27.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Eiter, T., Gottlob, G.: On the computational cost of disjunctive logic programming: propositional case. Ann. Math. Artif. Intell. 15(3/4), 289–323 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Eiter, T., Fink, M., Ianni, G., Krennwallner, T., Redl, C., Schüller, P.: A model building framework for answer set programming with external computations. In: Theory and Practice of Logic Programming (2015)., doi: 10.1017/S1471068415000113
  30. 30.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C.: Conflict-driven ASP solving with external sources. Theory Pract. Logic Program. 12(4–5), 659–679 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C.: Liberal safety criteria for HEX-programs. In: des Jardins, M., Littman, M. (eds.) AAAI Conference on Artificial Intelligence (AAAI). AAAI Press (2013)Google Scholar
  32. 32.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C.: Domain expansion for ASP-programs with external sources. Technical report INFSYS RR-1843-14-02, Institut für Informationssysteme, Technische Universität Wien, A-1040 Vienna, Austria, September 2014Google Scholar
  33. 33.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C.: HEX-programs with existential quantification. In: International Conference on Applications of Declarative Programming and Knowledge Management (INAP) (2014)Google Scholar
  34. 34.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C.: Domain expansion for ASP-programs with external sources. Artif. Intell. 233, 84–121 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Eiter, T., Fink, M., Krennwallner, T., Redl, C., Schüller, P.: Efficient HEX-program evaluation based on unfounded sets. J. Artif. Intell. Res. 49, 269–321 (2014)MathSciNetzbMATHGoogle Scholar
  36. 36.
    Eiter, T., Fink, M., Schüller, P., Weinzierl, A.: Finding explanations of inconsistency in multi-context systems. Artif. Intell. 216, 233–274 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Eiter, T., Gottlob, G.: On the computational cost of disjunctive logic programming: propositional case. Ann. Math. Artif. Intell. 15(3–4), 289–323 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Eiter, T., Gottlob, G., Veith, H.: Generalized quantifiers in logic programs. In: Väänänen, J. (ed.) ESSLLI 1997. LNCS, vol. 1754, pp. 72–98. Springer, Heidelberg (1999). doi: 10.1007/3-540-46583-9_4 CrossRefGoogle Scholar
  39. 39.
    Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: a primer. In: Reasoning Web Summer School, pp. 40–110 (2009)Google Scholar
  40. 40.
    Eiter, T., Ianni, G., Krennwallner, T., Schindlauer, R.: Exploiting conjunctive queries in description logic programs. Ann. Math. Artif. Intell. 53(1–4), 115–152 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R., Tompits, H.: Combining answer set programming with description logics for the semantic web. Artif. Intell. 172(12–13), 1495–1539 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 90–96. Professional Book Center (2005)Google Scholar
  43. 43.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: Effective integration of declarative rules with external evaluations for semantic-web reasoning. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 273–287. Springer, Heidelberg (2006). doi: 10.1007/11762256_22 CrossRefGoogle Scholar
  44. 44.
    Eiter, T., Kaminski, T., Redl, C., Weinzierl, A.: Exploiting partial assignments for efficient evaluation of answer set programs with external source access. In: IJCAI, pp. 1058–1065. IJCAI/AAAI Press (2016)Google Scholar
  45. 45.
    Eiter, T., Krennwallner, T., Redl, C.: HEX-programs with nested program calls. In: Tompits, H., Abreu, S., Oetsch, J., Pührer, J., Seipel, D., Umeda, M., Wolf, A. (eds.) INAP/WLP -2011. LNCS (LNAI), vol. 7773, pp. 269–278. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41524-1_15 CrossRefGoogle Scholar
  46. 46.
    Eiter, T., Mehuljic, M., Redl, C., Schüller, P.: User guide: dlvhex 2.x. Technical report INFSYS RR-1843-15-05, Vienna University of Technology, Institute for Information Systems (2015)Google Scholar
  47. 47.
    Eiter, T., Redl, C., Schüller, P.: Problem solving using the HEX family. In: Beierle, C., Brewka, G., Thimm, M. (eds.) Computational Models of Rationality - Essays Dedicated to Gabriele Kern-Isberner on the Occasion of her 60th Birthday, Tributes, pp. 150–174. College Publications, January 2016Google Scholar
  48. 48.
    Erdem, E., Gelfond, M., Leone, N.: Applications of answer set programming. AI Mag. 37(3), 53–68 (2016)CrossRefGoogle Scholar
  49. 49.
    Erdem, E., Patoglu, V., Schüller, P.: A systematic analysis of levels of integration between low-level reasoning and task planning. AI Commun. 29(2), 319–349 (2016)MathSciNetCrossRefGoogle Scholar
  50. 50.
    Faber, W., Leone, N., Pfeifer, G.: Recursive aggregates in disjunctive logic programs: semantics and complexity. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 200–212. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30227-8_19 CrossRefGoogle Scholar
  51. 51.
    Faber, W., Leone, N., Pfeifer, G.: Semantics and complexity of recursive aggregates in answer set programming. Artif. Intell. 175(1), 278–298 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    Fink, M., Germano, S., Ianni, G., Redl, C., Schüller, P.: ActHEX: implementing HEX programs with action atoms. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS (LNAI), vol. 8148, pp. 317–322. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40564-8_31 CrossRefGoogle Scholar
  53. 53.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers (2012)Google Scholar
  54. 54.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Wanko, P.: Theory solving made easy with clingo 5. In: ICLP (Technical Communications), vol. 52. OASICS, pp. 2:1–2:15. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2016)Google Scholar
  55. 55.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + control: Preliminary report. CoRR, abs/1405.3694 (2014)Google Scholar
  56. 56.
    Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: the potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)MathSciNetzbMATHGoogle Scholar
  57. 57.
    Gebser, M., Kaufmann, B., Schaub, T.: Conflict-driven answer set solving: from theory to practice. Artif. Intell. 187–188, 52–89 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  58. 58.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Logic Programming: Proceedings of the 5th International Conference and Symposium, pp. 1070–1080. MIT Press (1988)Google Scholar
  59. 59.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. Next Gener. Comput. 9(3–4), 365–386 (1991)CrossRefzbMATHGoogle Scholar
  60. 60.
    Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)zbMATHGoogle Scholar
  61. 61.
    Ghidini, C., Giunchiglia, F.: Local models semantics, or contextual reasoning = locality + compatibility. Artif. Intell. 127(2), 221–259 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    Giunchiglia, F., Serafini, L.: Multilanguage hierarchical logics or: how we can do without modal logics. Artif. Intell. 65(1), 29–70 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    Havur, G., Ozbilgin, G., Erdem, E., Patoglu, V.: Geometric rearrangement of multiple movable objects on cluttered surfaces: a hybrid reasoning approach. In: International Conference on Robotics and Automation (ICRA), pp. 445–452 (2014)Google Scholar
  64. 64.
    Heflin, J., Munoz-Avila, H.: LCW-based agent planning for the semantic web. In: Pease, A. (ed.) Ontologies and the Semantic Web. number WS-02-11 in AAAI Technical report, pp. 63–70. AAAI Press, Menlo Park, CA (2002)Google Scholar
  65. 65.
    Hoehndorf, R., Loebe, F., Kelso, J., Herre, H.: Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies. BMC Bioinformatics 8(1), 377 (2007)CrossRefGoogle Scholar
  66. 66.
    Janhunen, T., Liu, G., Niemelä, I.: Tight integration of non-ground answer set programming and satisfiability modulo theories. In: Cabalar, P., Mitchell, D., Pearce, D., Ternovska, E. (eds.) Informal Proceedings of the 1st Workshop on Grounding and Transformations for Theories with Variables (GTTV 2011), LPNMR, Vancouver, BC, Canada, 16 May 2011, pp. 1–14 (2013)Online available at
  67. 67.
    Kaminski, R., Schaub, T., Wanko, P.: A tutorial on hybrid answer set solving with clingo. In: Reasoning Web Summer School (2017, to appear)Google Scholar
  68. 68.
    Lassila, O., Swick, R.R.: Resource Description Framework (RDF) model and syntax specification (1999).
  69. 69.
    Lee, J., Meng, Y.: Answer set programming modulo theories and reasoning about continuous changes. In: Rossi, F. (ed.) IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 3–9 August 2013, pp. 990–996. IJCAI/AAAI (2013)Google Scholar
  70. 70.
    Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV system for knowledge representation and reasoning. ACM Trans. Comput. Logic (TOCL) 7(3), 499–562 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  71. 71.
    Lierler, Y.: Relating constraint answer set programming languages and algorithms. Artif. Intell. 207, 1–22 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  72. 72.
    Lierler, Y., Maratea, M., Ricca, F.: Systems, engineering environments, and competitions. AI Mag. 37(3), 45–52 (2016)CrossRefGoogle Scholar
  73. 73.
    Lifschitz, V.: Answer set programming and plan generation. Artif. Intell. 138, 39–54 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  74. 74.
    Lifschitz, V.: Thirteen definitions of a stable model. In: Blass, A., Dershowitz, N., Reisig, W. (eds.) Fields of Logic and Computation. LNCS, vol. 6300, pp. 488–503. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15025-8_24 CrossRefGoogle Scholar
  75. 75.
    Lin, F., Zhao, Y.: ASSAT: computing answer sets of a logic program by SAT solvers. Artif. Intell. 157(1–2), 115–137 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  76. 76.
    Liu, G., Janhunen, T., Niemelä, I.: Answer set programming via mixed integer programming. In: Brewka, G., Eiter, T., McIlraith, S.A. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the Thirteenth International Conference, KR 2012, Rome, Italy, 10–14 June 2012. AAAI Press (2012)Google Scholar
  77. 77.
    Marek, V.W., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K.R., Marek, V.W., Truszczynski, M., Warren, D.S. (eds.) The Logic Programming Paradigm - A 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  78. 78.
    Marek, W., Truszczyński, M.: Autoepistemic logic. J. ACM 38(3), 588–619 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  79. 79.
    May, W., Alferes, J.J., Amador, R.: Active rules in the semantic web: dealing with language heterogeneity. In: Adi, A., Stoutenburg, S., Tabet, S. (eds.) RuleML 2005. LNCS, vol. 3791, pp. 30–44. Springer, Heidelberg (2005). doi: 10.1007/11580072_4 CrossRefGoogle Scholar
  80. 80.
    McCarthy, J.: Notes on formalizing context. In: Bajcsy, R. (ed.) Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambéry, France, 28 August - 3 September 1993, pp. 555–562. Morgan Kaufmann (1993)Google Scholar
  81. 81.
    Mellarkod, V.S., Gelfond, M., Zhang, Y.: Integrating answer set programming and constraint logic programming. Ann. Math. Artif. Intell. 53(1–4), 251–287 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  82. 82.
    Mosca, A., Bernini, D.: Ontology-driven geographic information system and dlvhex reasoning for material culture analysis. In: Italian Workshop RiCeRcA at ICLP (2008)Google Scholar
  83. 83.
    Boris Motik and Riccardo Rosati. Reconciling description logics and rules. J. ACM, 57(5):30:1–30:62, 2010Google Scholar
  84. 84.
    Niemelä, I.: Logic programming with stable model semantics as constraint programming paradigm. Annals of Mathematics and Artificial Intelligenc 25(3–4), 241–273 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  85. 85.
    Nieuwenhuis, R., Oliveras, A., Tinelli, C.: Solving SAT and SAT modulo theories: From an abstract Davis-Putnam-Logemann-Loveland procedure to DPLL(T). J. ACM 53(6), 937–977 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  86. 86.
    Ostrowski, M., Schaub, T.: ASP modulo CSP: the clingcon system. Theory Pract. Logic Program. (TPLP) 12(4–5), 485–503 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  87. 87.
    Polleres, A.: From SPARQL to rules (and back). In: International Conference on World Wide Web (WWW), pp. 787–796. ACM (2007)Google Scholar
  88. 88.
    Redl, C.: Development of a belief merging framewerk for dlvhex. Master’s thesis, Vienna University of Technology, A-1040 Vienna, Karlsplatz 13 (2010)Google Scholar
  89. 89.
    Redl, C.: Answer set programming with external sources: algorithms and efficient evaluation. PhD thesis, Vienna University of Technology (2014)Google Scholar
  90. 90.
    Redl, C.: The dlvhex system for knowledge representation: recent advances (system description). TPLP 16(5–6), 866–883 (2016)MathSciNetGoogle Scholar
  91. 91.
    Redl, C., Eiter, T., Krennwallner, T.: Declarative belief set merging using merging plans. In: Rocha, R., Launchbury, J. (eds.) PADL 2011. LNCS, vol. 6539, pp. 99–114. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-18378-2_10 CrossRefGoogle Scholar
  92. 92.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  93. 93.
    De Rosis, A.F., Eiter, T., Redl, C., Ricca, F.: Constraint answer set programming based on HEX-programs. In: Eighth Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2015), 31 August 2015, Cork, Ireland, August 2015. Accepted for publicationGoogle Scholar
  94. 94.
    Schindlauer, R.: Answer set programming for the semantic web. PhD thesis, Vienna University of Technology, Vienna, Austria (2006)Google Scholar
  95. 95.
    Schüller, P., Weinzierl, A.: Answer set application programming: a case study on Tetris. In: De Vos, M., Eiter, T., Lierler, Y., Toni, F. (eds.) International Conference on Logic Programming (ICLP), Technical Communications, vol. 1433 (2015).
  96. 96.
    Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artif. Intell. 138(1–2), 181–234 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  97. 97.
    Susman, B., Lierler, Y.: SMT-based constraint answer set solver EZSMT (system description). In: Carro, M., King, A., Saeedloei, N., De Vos, M. (eds.) Technical Communications of the 32nd International Conference on Logic Programming, ICLP 2016 TCs, 16–21 October 2016, New York City, USA, vol. 52. OASICS, pp. 1:1–1:15. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2016)Google Scholar
  98. 98.
    Terracina, G., Francesco, E., Panetta, C., Leone, N.: Enhancing a DLP system for advanced database applications. In: Calvanese, D., Lausen, G. (eds.) RR 2008. LNCS, vol. 5341, pp. 119–134. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88737-9_10 CrossRefGoogle Scholar
  99. 99.
    Terracina, G., Leone, N., Lio, V., Panetta, C.: Experimenting with recursive queries in database and logic programming systems. TPLP 8(2), 129–165 (2008)MathSciNetzbMATHGoogle Scholar
  100. 100.
    Zakraoui, J., Zagler, W.: A method for generating CSS to improve web accessibility for old users. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012. LNCS, vol. 7382, pp. 329–336. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31522-0_50 CrossRefGoogle Scholar
  101. 101.
    Zirtiloǧlu, H., Yolum, P.: Ranking semantic information for e-government: complaints management. In: International Workshop on Ontology-supported business intelligence (OBI). ACM (2008)Google Scholar

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Authors and Affiliations

  1. 1.Institut für Informationssysteme, Knowledge Based Systems GroupTechnische Universität WienViennaAustria
  2. 2.Department of Computer Engineering, Faculty of EngineeringMarmara UniversityIstanbulTurkey

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