Probabilistic-Logical Web Data Integration

  • Mathias Niepert
  • Jan Noessner
  • Christian Meilicke
  • Heiner Stuckenschmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6848)

Abstract

The integration of both distributed schemas and data repositories is a major challenge in data and knowledge management applications. Instances of this problem range from mapping database schemas to object reconciliation in the linked open data cloud. We present a novel approach to several important data integration problems that combines logical and probabilistic reasoning. We first provide a brief overview of some of the basic formalisms such as description logics and Markov logic that are used in the framework. We then describe the representation of the different integration problems in the probabilistic-logical framework and discuss efficient inference algorithms. For each of the applications, we conducted extensive experiments on standard data integration and matching benchmarks to evaluate the efficiency and performance of the approach. The positive results of the evaluation are quite promising and the flexibility of the framework makes it easily adaptable to other real-world data integration problems.

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References

  1. 1.
    Albagli, S., Ben-Eliyahu-Zohary, R., Shimony, S.E.: Markov network based ontology matching. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1884–1889 (2009)Google Scholar
  2. 2.
    Bechhofer, S., Horrocks, I., Turi, D.: The OWL instance store: System description. In: Nieuwenhuis, R. (ed.) CADE 2005. LNCS (LNAI), vol. 3632, pp. 177–181. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Bhattacharya, I., Getoor, L.: Entity resolution in graphs. In: Mining Graph Data, Wiley, Chichester (2006)Google Scholar
  4. 4.
    Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1-2), 353–367 (1996)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Costa, P.C.G., Laskey, K.B.: Pr-owl: A framework for probabilistic ontologies. In: Bennett, B., Fellbaum, C. (eds.) Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS). Frontiers in Artificial Intelligence and Applications, pp. 237–249. IOS Press, Amsterdam (2006)Google Scholar
  6. 6.
    Cruz, I.F., Stroe, C., Caci, M., Caimi, F., Palmonari, M., Antonelli, F.P., Keles, U.C.: Using AgreementMaker to Align Ontologies for OAEI 2010. In: Proceedings of the 5th Workshop on Ontology Matching (2010)Google Scholar
  7. 7.
    Cruz, I., Palandri, F., Antonelli, Stroe, C.: Efficient selection of mappings and automatic quality-driven combination of matching methods. In: Proceedings of the ISWC 2009 Workshop on Ontology Matching (2009)Google Scholar
  8. 8.
    David, J., Guillet, F., Briand, H.: Matching directories and OWL ontologies with AROMA. In: Proceedings of the 15th Conference on Information and knowledge management (2006)Google Scholar
  9. 9.
    Ding, L., Kolari, P., Ding, Z., Avancha, S.: Bayesowl: Uncertainty modeling in semantic web ontologies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web, Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Ding, L., Finin, T.W.: Characterizing the semantic web on the web. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 242–257. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Euzenat, J., Hollink, A.F.L., Joslyn, C., Malaisé, V., Meilicke, C., Pane, A.N.J., Scharffe, F., Shvaiko, P., Spiliopoulos, V., Stuckenschmidt, H., Sváb-Zamazal, O., Svátek, V., dos Santos, C.T., Vouros, G.: Results of the ontology alignment evaluation initiative 2009. In: Proceedings of the ISWC 2009 workshop on Ontology Matching (2009)Google Scholar
  12. 12.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  13. 13.
    Euzenat, J., et al.: First Results of the Ontology Alignment Evaluation Initiative 2010. In: Proceedings of the 5th Workshop on Ontology Matching (2010)Google Scholar
  14. 14.
    Fellegi, I., Sunter, A.: A theory for record linkage. Journal of the American Statistical Association 64(328), 1183–1210 (1969)CrossRefMATHGoogle Scholar
  15. 15.
    Ferrara, A., Lorusso, D., Montanelli, S., Varese, G.: Towards a Benchmark for Instance Matching. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Ferrara, A., Montanelli, S., Noessner, J., Stuckenschmidt, H.: Benchmarking Matching Applications on the Semantic Web. In: The Semantic Web: Research and Applications (2011)Google Scholar
  17. 17.
    Giugno, R., Lukasiewicz, T.: P-\(\mathcal{SHOQ}({\bf D})\): A probabilistic extension of \(\mathcal{SHOQ}({\bf D})\) for probabilistic ontologies in the semantic web. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, p. 86. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Heinsohn, J.: A hybrid approach for modeling uncertainty in terminological logics. In: Kruse, R., Siegel, P. (eds.) ECSQAU 1991 and ECSQARU 1991. LNCS, vol. 548, pp. 198–205. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  19. 19.
    Holi, M., Hyvönen, E.: Modeling uncertainty in semantic web taxonomies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web, Springer, Heidelberg (2006)Google Scholar
  20. 20.
    Hu, W., Chen, J., Cheng, G., Qu, Y.: ObjectCoref & Falcon-AO: Results for OAEI 2010. In: Proceedings of the 5th International Ontology Matching Workshop (2010)Google Scholar
  21. 21.
    Jaeger, M.: Probabilistic reasoning in terminological logics. In: Doyle, J., Sandewall, E., Torasso, P. (eds.) Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, pp. 305–316. Morgan Kaufmann, San Francisco (1994)CrossRefGoogle Scholar
  22. 22.
    Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: ASMOV: Results for OAEI 2010. Ontology Matching, 126 (2010)Google Scholar
  23. 23.
    Jean-Marya, Y.R., Patrick Shironoshitaa, E., Kabuka, M.R.: Ontology matching with semantic verification. Web Semantics 7(3) (2009)Google Scholar
  24. 24.
    Koller, D., Levy, A., Pfeffer, A.: P-classic: A tractable probabilistic description logic. In: Proceedings of the 14th AAAI Conference on Artificial Intelligence (AAAI 1997), pp. 390–397 (1997)Google Scholar
  25. 25.
    Laskey, K.B., Costa, P.C.G.: Of klingons and starships: Bayesian logic for the 23rd century. In: Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, pp. 346–353. AUAI Press (2005)Google Scholar
  26. 26.
    Levenshtein, V.I.: Binary codes capable of correcting deletions and insertions and reversals. In: Doklady Akademii Nauk SSSR, pp. 845–848 (1965)Google Scholar
  27. 27.
    Li, L., Horrocks, I.: A software framework for matchmaking based on semantic web technology. International Journal of Electronic Commerce 8(4), 39 (2004)Google Scholar
  28. 28.
    Meilicke, C., Stuckenschmidt, H.: Analyzing mapping extraction approaches. In: Proceedings of the Workshop on Ontology Matching, Busan, Korea (2007)Google Scholar
  29. 29.
    Meilicke, C., Stuckenschmidt, H.: An efficient method for computing alignment diagnoses. In: Proceedings of the International Conference on Web Reasoning and Rule Systems, Chantilly, Virginia, USA, pp. 182–196 (2009)Google Scholar
  30. 30.
    Meilicke, C., Tamilin, A., Stuckenschmidt, H.: Repairing ontology mappings. In: Proceedings of the Conference on Artificial Intelligence, Vancouver, Canada, pp. 1408–1413 (2007)Google Scholar
  31. 31.
    Melnik, S., Garcia-Molina, H., Rahm., E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of ICDE, pp. 117–128 (2002)Google Scholar
  32. 32.
    Meza-Ruiz, I., Riedel, S.: Multilingual semantic role labelling with markov logic. In: Proceedings of the Conference on Computational Natural Language Learning, pp. 85–90 (2009)Google Scholar
  33. 33.
    Niepert, M.: A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (2010)Google Scholar
  34. 34.
    Niepert, M., Meilicke, C., Stuckenschmidt, H.: A Probabilistic-Logical Framework for Ontology Matching. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)Google Scholar
  35. 35.
    Niepert, M., Noessner, J., Stuckenschmidt, H.: Log-Linear Description Logics. In: Proceedings of the International Joint Conference on Artificial Intelligence (2011)Google Scholar
  36. 36.
    Noessner, J., Niepert, M.: CODI: Combinatorial Optimization for Data Integration–Results for OAEI 2010. In: Proceedings of the 5th Workshop on Ontology Matching (2010)Google Scholar
  37. 37.
    Noessner, J., Niepert, M., Meilicke, C., Stuckenschmidt, H.: Leveraging Terminological Structure for Object Reconciliation. In: The Semantic Web: Research and Applications, pp. 334–348 (2010)Google Scholar
  38. 38.
    Pan, R., Ding, Z., Yu, Y., Peng, Y.: A bayesian network approach to ontology mapping. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 563–577. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  39. 39.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2) (2006)Google Scholar
  40. 40.
    Riedel, S.: Improving the accuracy and efficiency of map inference for markov logic. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (2008)Google Scholar
  41. 41.
    Roth, D., Yih, W.-t.: Integer linear programming inference for conditional random fields. In: Proceedings of ICML, pp. 736–743 (2005)Google Scholar
  42. 42.
    Saïs, F., Pernelle, N., Rousset, M.-C.: Combining a logical and a numerical method for data reconciliation. Journal on Data Semantics 12, 66–94 (2009)CrossRefGoogle Scholar
  43. 43.
    Schrijver, A.: Theory of Linear and Integer Programming. Wiley, Chichester (1998)MATHGoogle Scholar
  44. 44.
    Shavlik, J., Natarajan, S.: Speeding up inference in markov logic networks by preprocessing to reduce the size of the resulting grounded network. In: Proceedings of the 21st International Joint Conference on Artifical intelligence, pp. 1951–1956 (2009)Google Scholar
  45. 45.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Journal of Web Semantics 5(2), 51–53 (2007)CrossRefGoogle Scholar
  46. 46.
    Stoermer, H., Rassadko, N.: Results of OKKAM feature based entity matching algorithm for instance matching contest of OAEI 2009. In: Proceedings of the ISWC 2009 Workshop on Ontology Matching (2009)Google Scholar
  47. 47.
    Stuckenschmidt, H., van Harmelen, F.: Information Sharing on the Semantic Web. Advanced Information and Knowledge Processing Series. Springer, Heidelberg (2005)CrossRefMATHGoogle Scholar
  48. 48.
    Stuckenschmidt, H.: A Semantic Similarity Measure for Ontology-Based Information. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS, vol. 5822, pp. 406–417. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  49. 49.
    Svab, O., Svatek, V., Berka, P., Rak, D., Tomasek, P.: Ontofarm: Towards an experimental collection of parallel ontologies. In: Poster Track of ISWC, Galway, Ireland (2005)Google Scholar
  50. 50.
    Taskar, B., Chatalbashev, V., Koller, D., Guestrin, C.: Learning structured prediction models: a large margin approach. In: Proceedings of ICML, pp. 896–903 (2005)Google Scholar
  51. 51.
    Tsarkov, D., Riazanov, A., Bechhofer, S., Horrocks, I.: Using vampire to reason with OWL. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 471–485. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  52. 52.
    Wang, Z., Zhang, X., Hou, L., Zhao, Y., Li, J., Qi, Y., Tang, J.: RiMOM Results for OAEI 2010. Ontology Matching, 195 (2010)Google Scholar
  53. 53.
    Wu, F., Weld, D.S.: Automatically refining the wikipedia infobox ontology. In: Proceeding of the International World Wide Web Conference, pp. 635–644 (2008)Google Scholar
  54. 54.
    Yang, Y., Calmet, J.: Ontobayes: An ontology-driven uncertainty model. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC 2005), pp. 457–463 (2005)Google Scholar
  55. 55.
    Yelland, P.M.: An alternative combination of bayesian networks and description logics. In: Cohn, A., Giunchiglia, F., Selman, B. (eds.) Proceedings of of the 7th International Conference on Knowledge Representation (KR 2000), pp. 225–234. Morgan Kaufman, San Francisco (2002)Google Scholar
  56. 56.
    Zhang, X., Zhong, Q., Shi, F., Li, J., Tang, J.: RiMOM results for OAEI 2009. In: Proceedings of the ISWC 2009 workshop on ontology matching (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mathias Niepert
    • 1
  • Jan Noessner
    • 1
  • Christian Meilicke
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.KR & KM Research GroupUniversity of MannheimMannheimGermany

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