Consistency and Provenance in Rule Processing

  • Eric Jui-Yi Kao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7018)


Open collections of data and rules on the web are typically characterized by heterogeneous quality and imperfect consistency. In reasoning with data and rules on the web, it is important to know where an answer comes from (provenance) and whether the it is reasonable considering the inconsistencies (inconsistency-tolerance). In this paper, I draw attention to the idea that provenance and inconsistency-tolerance play mutually supporting roles under the theme of reasoning with imperfect information on the web. As a specific example, I make use of basic provenance information to avoid unreasonable answers in reasoning with rules and inconsistent data.


Logic Program Horn Clause Provenance Information Rule Processing Datalog 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of databases (1995)Google Scholar
  2. 2.
    Agrawal, P., Benjelloun, O., Sarma, A.D., Hayworth, C., Nabar, S., Sugihara, T., Widom, J.: Trio: A system for data, uncertainty, and lineage. In: 32nd International Conference on Very Large Data Bases, VLDB 2006 (demonstration description) (September 2006),
  3. 3.
    Alejandro Gmez, S., Ivn Chesevar, C., Simari, G.R.: Reasoning with inconsistent ontologies through argumentation. Applied Artificial Intelligence 24(1-2), 102–148 (2010), Scholar
  4. 4.
    Arenas, M., Bertossi, L., Chomicki, J.: Specifying and querying database repairs using logic programs with exceptions. In: Flexible Query Answering Systems. Recent Developments, pp. 27–41. Springer, Heidelberg (2000)Google Scholar
  5. 5.
    Arenas, M., Bertossi, L., Chomicki, J.: Answer sets for consistent query answering in inconsistent databases. Theory and Practice of Logic Programming 3(4), 393–424 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Barcel, P., Bertossi, L.: Repairing databases with annotated predicate logic. In: Ninth International Workshop on Non-Monotonic Reasoning (NMR 2002), Special Session: Changing and Integrating Information: From Theory to Practice, pp. 160–170. Morgan Kaufmann Publishers (2002)Google Scholar
  7. 7.
    Barcel, P., Bertossi, L.: Logic Programs for Querying Inconsistent Databases. In: Dahl, V. (ed.) PADL 2003. LNCS, vol. 2562, pp. 208–222. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Benjelloun, O., Das Sarma, A., Halevy, A., Theobald, M., Widom, J.: Databases with uncertainty and lineage. The VLDB Journal 17, 243–264 (2008), Scholar
  9. 9.
    Bravo, L., Bertossi, L.: Logic programs for consistently querying data integration systems. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 10–15 (2003)Google Scholar
  10. 10.
    Chebotko, A., Lu, S., Fei, X., Fotouhi, F.: Rdfprov: A relational rdf store for querying and managing scientific workflow provenance. Data Knowl. Eng. 69, 836–865 (2010), Scholar
  11. 11.
    Ding, L., Michaelis, J., McCusker, J., McGuinness, D.L.: Linked provenance data: A semantic web-based approach to interoperable workflow traces. Future Gener. Comput. Syst. 27, 797–805 (2011), Scholar
  12. 12.
    Eiter, T.: Data Integration and Answer Set Programming. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 13–25. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Eiter, T., Fink, M., Greco, G., Lembo, D.: Optimization methods for logic-based query answering from inconsistent data integration systems (2005)Google Scholar
  14. 14.
    Elvang-Gøransson, M., Hunter, A.: Argumentative logics: Reasoning with classically inconsistent information. Data Knowl. Eng. 16(2), 125–145 (1995)CrossRefzbMATHGoogle Scholar
  15. 15.
    Espil, M.M., Vaisman, A.A., Terribile, L.: Revising data cubes with exceptions: A rule-based perspective (2002)Google Scholar
  16. 16.
    Fan, H., Poulovassilis, A.: Tracing data lineage using schema transformation pathways. In: Knowledge Transformation For The Semantic Web, pp. 64–79. IOS Press (2002)Google Scholar
  17. 17.
    Hinrichs, T.L., Kao, J.Y., Genesereth, M.: Inconsistency-tolerant reasoning with classical logic and large databases. In: Proc. of the Eighth Symposium on Abstraction, Reformulation, and Approximation (2009)Google Scholar
  18. 18.
    Huang, Z., van Harmelen, F., ten Teije, A.: Reasoning with inconsistent ontologies. In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 454–459 (August 2005)Google Scholar
  19. 19.
    Kao, E.J.Y., Genesereth, M.: Query rewriting with filtering constraints. Tech. Rep. LG-2009-02, Stanford University, Stanford, CA (2009), (updated July 2011)
  20. 20.
    Kassoff, M., Genesereth, M.: Predicalc: A logical spreadsheet management system. Knowl. Eng. Rev. 22(3), 281–295 (2007)Google Scholar
  21. 21.
    Kassoff, M., Genesereth, M.R.: Paraconsistent inference from data using ω-existential entailment. DALI: Workshop on Data, Logic and Inconsistency (2011)Google Scholar
  22. 22.
    Kassoff, M., Zen, L.M., Garg, A., Genesereth, M.: Predicalc: a logical spreadsheet management system. In: VLDB 2005: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 1247–1250. VLDB Endowment (2005)Google Scholar
  23. 23.
    Li, D., Lin, Y., Huang, H., Tian, X.: Linear reduction reasoning with inconsistent ontology. In: 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO), pp. 795–798 (April 2011)Google Scholar
  24. 24.
    Lifschitz, V.: What is answer set programming? In: Proceedings of the 23rd National Conference on Artificial Intelligence, vol. 3, pp. 1594–1597. AAAI Press (2008),
  25. 25.
    Moreau, L.: Provenance-based reproducibility in the semantic web. Journal of Web Semantics (February 2011),
  26. 26.
    Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E., den Bussche, J.V.: The open provenance model core specification (v1.1). Future Generation Computer Systems 27(6), 743–756 (2011), Scholar
  27. 27.
    Dal Palù, A., Dovier, A., Pontelli, E., Rossi, G.: Answer Set Programming with Constraints Using Lazy Grounding. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 115–129. Springer, Heidelberg (2009), Scholar
  28. 28.
    Schobach, S., Corner, R.: Non-standard reasoning services for the debugging of description logic terminology. In: IJCAI (2003)Google Scholar
  29. 29.
    Zlatareva, N.P.: Supporting uncertainty and inconsistency in semantic web applications. In: FLAIRS Conference (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eric Jui-Yi Kao
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUnited States of America

Personalised recommendations