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Statistical Relational Data Integration for Information Extraction

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8067)

Abstract

These lecture notes provide a brief overview of some state of the art large scale information extraction projects. Consequently, these projects are related to current research activities in the semantic web community. The majority of the learning algorithms developed for these information extraction projects are based on the lexical and syntactical processing of Wikipedia and large web corpora. Due to the size of the processed data and the resulting intractability of the associated inference problems existing knowledge representation formalism are often inadequate for the task. We will present recent advances in combining tractable logical and probabilistic models that bring statistical language processing and rule-based approaches closer together. With these lecture notes we hope to convince the attendees that there are numerous synergies and research agendas that can arise when uncertainty-based data-driven research meets rule-based schema-driven research. We also describe certain theoretical and practical advances in making probabilistic inference scale to very large problems.

Keywords

  • Resource Description Framework
  • Information Extraction
  • Description Logic
  • Ground Atom
  • Ontology Match

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.

These lecture notes are based on several previous publications of the author and his colleagues in conference proceedings such as AAAI, UAI, IJCAI, and ESWC.

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References

  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. Apsel, U., Brafman, R.: Exploiting uniform assignments in first-order mpe. In: Proceedings of UAI, pp. 74–83 (2012)

    Google Scholar 

  3. Asano, T.: An improved analysis of goemans and williamson’s lp-relaxation for max sat. Theoretical Computer Science 354(3), 339–353 (2006)

    MathSciNet  CrossRef  MATH  Google Scholar 

  4. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  5. Bengio, Y., LeCun, Y.: Scaling learning algorithms towards AI. In: Large Scale Kernel Machines. MIT Press (2007)

    Google Scholar 

  6. Berners-Lee, T.: Linked data – design issues (2006), http://www.w3.org/DesignIssues/LinkedData.html

  7. Bhattacharya, I., Getoor, L.: Entity resolution in graphs. In: Mining Graph Data. Wiley & Sons (2006)

    Google Scholar 

  8. Bizer, C., Heath, T., Berners-Lee, T.: Linked data – the story so far. International Journal on Semantic Web and Information Systems (2012)

    Google Scholar 

  9. Bödi, R., Herr, K., Joswig, M.: Algorithms for highly symmetric linear and integer programs. Mathematical Programming 137(1-2), 65–90 (2013)

    MathSciNet  CrossRef  MATH  Google Scholar 

  10. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)

    Google Scholar 

  11. Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1-2), 353–367 (1996)

    MathSciNet  CrossRef  Google Scholar 

  12. Bui, H.H., Huynh, T.N., Riedel, S.: Automorphism groups of graphical models and lifted variational inference. CoRR, abs/1207.4814 (2012)

    Google Scholar 

  13. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010), pp. 1306–1313 (2010)

    Google Scholar 

  14. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)

    Google Scholar 

  15. 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 (2006)

    Google Scholar 

  16. 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 

  17. Cruz, I., Antonelli, F.P., 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 

  18. 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 

  19. de Salvo Braz, R., Amir, E., Roth, D.: MPE and partial inversion in lifted probabilistic variable elimination. In: Proceedings of AAAI, pp. 1123–1130 (2006)

    Google Scholar 

  20. Diaconis, P.: Finite forms of de finetti’s theorem on exchangeability. Synthese 36(2), 271–281 (1977)

    MathSciNet  CrossRef  MATH  Google Scholar 

  21. 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 (2006)

    Google Scholar 

  22. Domingos, P., Jain, D., Kok, S., Lowd, D., Poon, H., Richardson, M.: Alchemy website (2012), http://alchemy.cs.washington.edu/ (last visit: November 22, 2012)

  23. Etzioni, O., Banko, M., Soderland, S., Weld, D.S.: Open information extraction from the web. Communications of the ACM 51(12), 68–74 (2008)

    CrossRef  Google Scholar 

  24. Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam, M.: Open information extraction: the second generation. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 3–10 (2011)

    Google Scholar 

  25. 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 

  26. Euzenat, J., Shvaiko, P.: Ontology matching. Springer (2007)

    Google Scholar 

  27. 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 

  28. Fellbaum, C.: WordNet. Springer (2010)

    Google Scholar 

  29. Fellegi, I., Sunter, A.: A theory for record linkage. Journal of the American Statistical Association 64(328), 1183–1210 (1969)

    CrossRef  MATH  Google Scholar 

  30. Ferrara, A., Lorusso, D., Montanelli, S., Varese, G.: Towards a Benchmark for Instance Matching. In: The 7th International Semantic Web Conference (2008)

    Google Scholar 

  31. Finetti, B.D.: Probability, induction and statistics: the art of guessing. Probability and mathematical statistics. Wiley (1972)

    Google Scholar 

  32. Giugno, R., Lukasiewicz, T.: P-shoq(d): A probabilistic extension of shoq(d) for probabilistic ontologies in the semantic web. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 86–97. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  33. Gogate, V., Domingos, P.: Probabilistic theorem proving. In: Proceedings of UAI, pp. 256–265 (2011)

    Google Scholar 

  34. 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)

    CrossRef  Google Scholar 

  35. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)

    MathSciNet  CrossRef  MATH  Google Scholar 

  36. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: A spatially and temporally enhanced knowledge base from wikipedia. Artificial Intelligence 194, 28–61 (2013)

    MathSciNet  CrossRef  MATH  Google Scholar 

  37. Holi, M., Hyvönen, E.: Modeling uncertainty in semantic web taxonomies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web. Springer (2006)

    Google Scholar 

  38. 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 

  39. Huynh, T.N., Mooney, R.J.: Max-margin weight learning for markov logic networks. In: Proceedings of EMCL PKDD, pp. 564–579 (2009)

    Google Scholar 

  40. 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 (1994)

    Google Scholar 

  41. Jean-Marya, Y.R., Patrick Shironoshitaa, E., Kabuka, M.R.: Ontology matching with semantic verification. Web Semantics 7(3) (2009)

    Google Scholar 

  42. Kautz, H., Selman, B., Jiang, Y.: A general stochastic approach to solving problems with hard and soft constraints. Satisfiability Problem: Theory and Applications 17 (1997)

    Google Scholar 

  43. Kersting, K., Ahmadi, B., Natarajan, S.: Counting belief propagation. In: Proceedings of UAI, pp. 277–284 (2009)

    Google Scholar 

  44. Kersting, K.: Lifted probabilistic inference. In: Proceedings of the 20th European Conference on Artificial Intelligence, pp. 33–38 (2012)

    Google Scholar 

  45. Kisynski, J., Poole, D.: Lifted aggregation in directed first-order probabilistic models. In: Proceedings of IJCAI, pp. 1922–1929 (2009)

    Google Scholar 

  46. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)

    Google Scholar 

  47. 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 

  48. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)

    Google Scholar 

  49. 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 

  50. Levenshtein, V.I.: Binary codes capable of correcting deletions and insertions and reversals. In: Doklady Akademii Nauk SSSR, pp. 845–848 (1965)

    Google Scholar 

  51. Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: Graphlab: A new framework for parallel machine learning. In: Proceedings of UAI, pp. 340–349 (2010)

    Google Scholar 

  52. Manola, F., Miller, E.: RDF primer. Technical report, WWW Consortium (February 2004), http://www.w3.org/TR/2004/REC-rdf-primer-20040210/

  53. Margot, F.: Exploiting orbits in symmetric ilp. Math. Program. 98(1-3), 3–21 (2003)

    MathSciNet  CrossRef  MATH  Google Scholar 

  54. Margot, F.: Symmetry in integer linear programming. In: 50 Years of Integer Programming 1958-2008, pp. 647–686. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  55. Meilicke, C., Stuckenschmidt, H.: Analyzing mapping extraction approaches. In: Proceedings of the Workshop on Ontology Matching, Busan, Korea (2007)

    Google Scholar 

  56. Meilicke, C., Stuckenschmidt, H.: An efficient method for computing alignment diagnoses. In: Polleres, A., Swift, T. (eds.) RR 2009. LNCS, vol. 5837, pp. 182–196. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  57. 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 

  58. 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 

  59. Mendes, P.N., Jakob, M., Bizer, C.: Dbpedia: A multilingual cross-domain knowledge base. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC), pp. 1813–1817 (2012)

    Google Scholar 

  60. 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 

  61. Milch, B., Zettlemoyer, L.S., Kersting, K., Haimes, M., Kaelbling, L.P.: Lifted probabilistic inference with counting formulas. In: Proceedings of AAAI, pp. 1062–1068 (2008)

    Google Scholar 

  62. Mitchell, T.M., Betteridge, J., Carlson, A., Hruschka, E., Wang, R.: Populating the semantic web by macro-reading internet text. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 998–1002. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  63. Mladenov, M., Ahmadi, B., Kersting, K.: Lifted linear programming. Journal of Machine Learning Research 22, 788–797 (2012)

    Google Scholar 

  64. 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 

  65. Niepert, M.: Markov chains on orbits of permutation groups. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), pp. 624–633 (2012)

    Google Scholar 

  66. Niepert, M.: Symmetry-aware maginal density estimation. In: Proceedings of the Conference on Artificial Intelligence (AAAI) (2013)

    Google Scholar 

  67. 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 

  68. Niepert, M., Meilicke, C., Stuckenschmidt, H.: Towards distributed mcmc inference in probabilistic knowledge bases. In: Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, pp. 1–6 (2012)

    Google Scholar 

  69. Niepert, M., Noessner, J., Meilicke, C., Stuckenschmidt, H.: Probabilistic-logical web data integration. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 504–533. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  70. Niepert, M., Noessner, J., Stuckenschmidt, H.: Log-Linear Description Logics. In: Proceedings of the International Joint Conference on Artificial Intelligence (2011)

    Google Scholar 

  71. Niu, F., Ré, C., Doan, A.H., Shavlik, J.: Tuffy: Scaling up statistical inference in markov logic networks using an rdbms. Proceedings of the VLDB Endowment 4(6), 373–384 (2011)

    CrossRef  Google Scholar 

  72. Niu, F., Zhang, C., Ré, C., Shavlik, J.: Deepdive: Web-scale knowledge-base construction using statistical learning and inference. In: Second Int.l Workshop on Searching and Integrating New Web Data Sources (2012)

    Google Scholar 

  73. Noessner, J., Niepert, M., Stuckenschmidt, H.: Coherent top-k ontology alignment for OWL EL. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 415–427. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  74. Noessner, J., Niepert, M., Stuckenschmidt, H.: RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. In: Proceedings of the Conference on Artificial Intelligence (AAAI) (2013)

    Google Scholar 

  75. Noessner, J., Niepert, M., Meilicke, C., Stuckenschmidt, H.: Leveraging Terminological Structure for Object Reconciliation. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 334–348. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  76. Ostrowski, J., Linderoth, J., Rossi, F., Smriglio, S.: Orbital branching. Math. Program. 126(1), 147–178 (2011)

    MathSciNet  CrossRef  MATH  Google Scholar 

  77. 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)

    CrossRef  Google Scholar 

  78. Poole, D.: First-order probabilistic inference. In: Proceedings of IJCAI, pp. 985–991 (2003)

    Google Scholar 

  79. Poon, H., Domingos, P.: Sum-product networks: A new deep architecture. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, pp. 337–346 (2011)

    Google Scholar 

  80. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2) (2006)

    Google Scholar 

  81. 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 

  82. Saïs, F., Pernelle, N., Rousset, M.-C.: Combining a logical and a numerical method for data reconciliation. In: Spaccapietra, S. (ed.) Journal on Data Semantics XII. LNCS, vol. 5480, pp. 66–94. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  83. Schoenmackers, S., Etzioni, O., Weld, D.S., Davis, J.: Learning first-order horn clauses from web text. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1088–1098 (2010)

    Google Scholar 

  84. 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 

  85. Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proceedings of AAAI, pp. 1094–1099 (2008)

    Google Scholar 

  86. 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 

  87. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)

    Google Scholar 

  88. 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)

    CrossRef  Google Scholar 

  89. Van den Broeck, G.: On the completeness of first-order knowledge compilation for lifted probabilistic inference. In: Proceedings of NIPS, pp. 1386–1394 (2011)

    Google Scholar 

  90. Venugopal, D., Gogate, V.: On lifting the gibbs sampling algorithm. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 1664–1672 (2012)

    Google Scholar 

  91. Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Silk - a link discovery framework for the web of data. In: Proceedings of the WWW 2009 Workshop on Linked Data on the Web (LDOW) (2009)

    Google Scholar 

  92. 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 

  93. 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 

  94. 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 (2000)

    Google Scholar 

  95. 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 

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Niepert, M. (2013). Statistical Relational Data Integration for Information Extraction. In: Rudolph, S., Gottlob, G., Horrocks, I., van Harmelen, F. (eds) Reasoning Web. Semantic Technologies for Intelligent Data Access. Reasoning Web 2013. Lecture Notes in Computer Science, vol 8067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39784-4_7

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