Introduction to Linked Data and Its Lifecycle on the Web

  • Axel-Cyrille Ngonga Ngomo
  • Sören Auer
  • Jens Lehmann
  • Amrapali Zaveri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8714)


With Linked Data, a very pragmatic approach towards achieving the vision of the Semantic Web has gained some traction in the last years. The term Linked Data refers to a set of best practices for publishing and interlinking structured data on the Web. While many standards, methods and technologies developed within by the Semantic Web community are applicable for Linked Data, there are also a number of specific characteristics of Linked Data, which have to be considered. In this article we introduce the main concepts of Linked Data. We present an overview of the Linked Data life-cycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, linking, enrichment as well as quality of Linked Data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of Linked Data. This article is an updated version of a similar lecture given at Reasoning Web Summer School 2013.


Resource Description Framework Link Data Inductive Logic Programming SPARQL Query Link Open Data 
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.


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  1. 1.
    Resource description framework (RDF): Concepts and abstract syntax. Technical report, W3C, 2 (2004)Google Scholar
  2. 2.
    Acosta, M., Zaveri, A., Simperl, E., Kontokostas, D., Auer, S., Lehmann, J.: Crowdsourcing linked data quality assessment. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 260–276. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Adida, B., Birbeck, M., McCarron, S., Pemberton, S.: RDFa in XHTML: Syntax and processing – a collection of attributes and processing rules for extending XHTML to support RDF. W3C Recommendation (October 2008),
  4. 4.
    Agichtein, E., Gravano, L.: Snowball: Extracting relations from large plain-text collections. In: ACM DL, pp. 85–94 (2000)Google Scholar
  5. 5.
    Agresti, A.: An Introduction to Categorical Data Analysis, 2nd edn. Wiley-Interscience (1997)Google Scholar
  6. 6.
    Amsler, R.: Research towards the development of a lexical knowledge base for natural language processing. SIGIR Forum 23, 1–2 (1989)CrossRefGoogle Scholar
  7. 7.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: 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)Google Scholar
  8. 8.
    Auer, S., Dietzold, S., Lehmann, J., Hellmann, S., Aumueller, D.: Triplify: Light-weight linked data publication from relational databases. In: Quemada, J., León, G., Maarek, Y.S., Nejdl, W. (eds.) Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, pp. 621–630. ACM (2009)Google Scholar
  9. 9.
    Auer, S., Dietzold, S., Riechert, T.: OntoWiki – A Tool for Social, Semantic Collaboration. 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. 736–749. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Auer, S., Herre, H.: A versioning and evolution framework for RDF knowledge bases. In: Virbitskaite, I., Voronkov, A. (eds.) PSI 2006. LNCS, vol. 4378, pp. 55–69. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Auer, S., Lehmann, J.: Making the web a data washing machine - creating knowledge out of interlinked data. Semantic Web Journal (2010)Google Scholar
  12. 12.
    Auer, S., Lehmann, J., Hellmann, S.: LinkedGeoData: Adding a spatial dimension to the web of data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 731–746. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Auer, S., Lehmann, J., Ngonga Ngomo, A.-C., Zaveri, A.: Introduction to linked data and its lifecycle on the web. In: Rudolph, S., Gottlob, G., Horrocks, I., van Harmelen, F. (eds.) Reasoning Weg 2013. LNCS, vol. 8067, pp. 1–90. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Auer, S., Weidl, M., Lehmann, J., Zaveri, A.J., Choi, K.-S.: I18n of semantic web applications. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 1–16. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Aumüller, D.: Semantic Authoring and Retrieval within a Wiki (WikSAR). In: Demo Session at the Second European Semantic Web Conference (ESWC 2005) (May 2005),
  16. 16.
    Baader, F., Diageo, C., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook, Cambridge (2003)Google Scholar
  17. 17.
    Baader, F., Ganter, B., Sattler, U., Sertkaya, B.: Completing description logic knowledge bases using formal concept analysis. In: IJCAI 2007. AAAI Press (2007)Google Scholar
  18. 18.
    Baader, F., Sertkaya, B., Turhan, A.-Y.: Computing the least common subsumer w.r.t. a background terminology. J. Applied Logic 5(3), 392–420 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Badea, L., Nienhuys-Cheng, S.-H.: A refinement operator for description logics. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 40–59. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  20. 20.
    Baxter, R., Christen, P., Churches, T.: A comparison of fast blocking methods for record linkage. In: KDD 2003 Workshop on Data Cleaning, Record Linkage, and Object Consolidation (2003)Google Scholar
  21. 21.
    Ben-David, D., Domany, T., Tarem, A.: Enterprise data classification using semantic web technologies. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 66–81. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Berners-Lee, T., Fielding, R.T., Masinter, L.: Uniform resource identifiers (URI): Generic syntax. Internet RFC 2396 (August 1998)Google Scholar
  23. 23.
    Bhagdev, R., Chapman, S., Ciravegna, F., Lanfranchi, V., Petrelli, D.: Hybrid search: Effectively combining keywords and semantic searches. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 554–568. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Bilenko, M., Kamath, B., Mooney, R.J.: Adaptive blocking: Learning to scale up record linkage. In: ICDM 2006, pp. 87–96. IEEE (2006)Google Scholar
  25. 25.
    Bizer, C.: Quality-Driven Information Filtering in the Context of Web-Based Information Systems. PhD thesis, Freie Universität Berlin (March 2007)Google Scholar
  26. 26.
    Bizer, C., Cyganiak, R.: Quality-driven information filtering using the wiqa policy framework. Web Semantics 7(1), 1–10 (2009)CrossRefGoogle Scholar
  27. 27.
    Bleiholder, J., Naumann, F.: Data fusion. ACM Comput. Surv. 41(1), 1–41 (2008)CrossRefGoogle Scholar
  28. 28.
    Bleiholder, J., Naumann, F.: Data fusion. ACM Computing Surveys (CSUR) 41(1), 1 (2008)CrossRefGoogle Scholar
  29. 29.
    Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. In: Readings in Machine Learning, pp. 201–204. Morgan Kaufmann (1990)Google Scholar
  30. 30.
    Böhm, C., Naumann, F., Abedjan, Z., Fenz, D., Grütze, T., Hefenbrock, D., Pohl, M., Sonnabend, D.: Profiling linked open data with ProLOD. In: ICDE Workshops, pp. 175–178. IEEE (2010)Google Scholar
  31. 31.
    Bonatti, P.A., Hogan, A., Polleres, A., Sauro, L.: Robust and scalable linked data reasoning incorporating provenance and trust annotations. Journal of Web Semantics 9(2), 165–201 (2011)CrossRefGoogle Scholar
  32. 32.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHGoogle Scholar
  33. 33.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  34. 34.
    Brickley, D., Guha, R.V.: RDF Vocabulary Description Language 1.0: RDF Schema. W3C recommendation, W3C (February 2004),
  35. 35.
    Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  36. 36.
    Bühmann, L., Lehmann, J.: Universal OWL axiom enrichment for large knowledge bases. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 57–71. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  37. 37.
    Carroll, J.J.: Signing RDF graphs. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 369–384. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  38. 38.
    Chang, C.-C., Lin, C.-J.: Libsvm - a library for support vector machines, The Weka classifier works with version 2.82 of LIBSVM (2001)Google Scholar
  39. 39.
    Chen, P., Garcia, W.: Hypothesis generation and data quality assessment through association mining. In: IEEE ICCI, pp. 659–666. IEEE (2010)Google Scholar
  40. 40.
    Cherix, D., Hellmann, S., Lehmann, J.: Improving the performance of the DL-learner SPARQL component for semantic web applications. In: Takeda, H., Qu, Y., Mizoguchi, R., Kitamura, Y. (eds.) JIST 2012. LNCS, vol. 7774, pp. 332–337. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  41. 41.
    Choi, N., Song, I.-Y., Han, H.: A survey on ontology mapping. SIGMOD Record 35(3), 34–41 (2006)CrossRefGoogle Scholar
  42. 42.
    Coates-Stephens, S.: The analysis and acquisition of proper names for the understanding of free text. Computers and the Humanities 26, 441–456 (1992), doi:10.1007/BF00136985CrossRefGoogle Scholar
  43. 43.
    Cohen, W.W., Borgida, A., Hirsh, H.: Computing least common subsumers in description logics. In: AAAI 1992, pp. 754–760 (1992)Google Scholar
  44. 44.
    Cohen, W.W., Hirsh, H.: Learning the CLASSIC description logic: Theoretical and experimental results. In: KR 1994, pp. 121–133. Morgan Kaufmann (1994)Google Scholar
  45. 45.
    Cornolti, M., Ferragina, P., Ciaramita, M.: A framework for benchmarking entity-annotation systems. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 249–260. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  46. 46.
    Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: EMNLP-CoNLL, pp. 708–716 (2007)Google Scholar
  47. 47.
    Curran, J.R., Clark, S.: Language independent ner using a maximum entropy tagger. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, Morristown, NJ, USA, vol. 4, pp. 164–167. Association for Computational Linguistics (2003)Google Scholar
  48. 48.
    d’Amato, C., Fanizzi, N., Esposito, F.: A note on the evaluation of inductive concept classification procedures. In: Gangemi, A., Keizer, J., Presutti, V., Stoermer, H. (eds.) SWAP 2008. CEUR Workshop Proceedings, vol. 426. (2008)Google Scholar
  49. 49.
    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
  50. 50.
    Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19, 1–16 (2007)CrossRefGoogle Scholar
  51. 51.
    Ermilov, T., Heino, N., Tramp, S., Auer, S.: OntoWiki Mobile – Knowledge Management in Your Pocket. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 185–199. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  52. 52.
    Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Knowledge-intensive induction of terminologies from metadata. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 441–455. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  53. 53.
    Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165, 91–134 (2005)CrossRefGoogle Scholar
  54. 54.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  55. 55.
    Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL concept learning in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  56. 56.
    Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., Berners-Lee, T.: Hypertext transfer protocol – http/1.1 (rfc 2616). Request For Comments (1999), (accessed July 7, 2006)
  57. 57.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, pp. 363–370. Association for Computational Linguistics, Morristown (2005)Google Scholar
  58. 58.
    Fleischhacker, D., Völker, J., Stuckenschmidt, H.: Mining RDF data for property axioms. In: Meersman, R., et al. (eds.) OTM 2012, Part II. LNCS, vol. 7566, pp. 718–735. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  59. 59.
    Flemming, A.: Quality characteristics of linked data publishing datasources. Master’s thesis, Humboldt-Universität zu Berlin (2010)Google Scholar
  60. 60.
    Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-specific keyphrase extraction. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 1999, pp. 668–673. Morgan Kaufmann Publishers Inc, San Francisco (1999)Google Scholar
  61. 61.
    Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  62. 62.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  63. 63.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Stanford University (1998)Google Scholar
  64. 64.
    Fürber, C., Hepp, M.: SWIQA - a semantic web information quality assessment framework. In: ECIS (2011)Google Scholar
  65. 65.
    Gama, J.: Functional trees 55(3), 219–250 (2004)Google Scholar
  66. 66.
    Gamble, M., Goble, C.: Quality, trust, and utility of scientific data on the web: Towards a joint model. In: ACM WebSci, pp. 1–8 (June 2011)Google Scholar
  67. 67.
    Gil, Y., Artz, D.: Towards content trust of web resources. Web Semantics 5(4), 227–239 (2007)CrossRefGoogle Scholar
  68. 68.
    Gil, Y., Ratnakar, V.: Trusting information sources one citizen at a time. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, p. 162. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  69. 69.
    Glaser, H., Millard, I.C., Sung, W.-K., Lee, S., Kim, P., You, B.-J.: Research on linked data and co-reference resolution. Technical report, University of Southampton (2009)Google Scholar
  70. 70.
    Golbeck, J.: Using trust and provenance for content filtering on the semantic web. In: Workshop on Models of Trust on the Web at the 15th World Wide Web Conference (2006)Google Scholar
  71. 71.
    Golbeck, J., Parsia, B., Hendler, J.: Trust networks on the semantic web. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS (LNAI), vol. 2782, pp. 238–249. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  72. 72.
    Grishman, R., Yangarber, R.: Nyu: Description of the Proteus/Pet system as used for MUC-7 ST. In: MUC-7. Morgan Kaufmann (1998)Google Scholar
  73. 73.
    Guéret, C., Groth, P., Stadler, C., Lehmann, J.: Assessing linked data mappings using network measures. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 87–102. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  74. 74.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  75. 75.
    Harabagiu, S., Bejan, C.A., Morarescu, P.: Shallow semantics for relation extraction. In: IJCAI, pp. 1061–1066 (2005)Google Scholar
  76. 76.
    Hartig, O.: Trustworthiness of data on the web. In: STI Berlin and CSW PhD Workshop, Berlin, Germany (2008)Google Scholar
  77. 77.
    Hartig, O., Zhao, J.: Using web data provenance for quality assessment. In: Freire, J., Missier, P., Sahoo, S.S. (eds.) SWPM. CEUR Workshop Proceedings, vol. 526, (2009)Google Scholar
  78. 78.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10, MIT Press (1998)Google Scholar
  79. 79.
    Heath, T., Bizer, C.: Linked Data - Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web:Theory and Technology, vol. 1. Morgan & Claypool (2011)Google Scholar
  80. 80.
    Heino, N., Dietzold, S., Martin, M., Auer, S.: Developing semantic web applications with the ontoWiki framework. In: Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S. (eds.) Networked Knowledge - Networked Media. SCI, vol. 221, pp. 61–77. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  81. 81.
    Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL class descriptions on very large knowledge bases. Int. J. Semantic Web Inf. Syst. 5(2), 25–48 (2009)CrossRefGoogle Scholar
  82. 82.
    Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL class expressions on very large knowledge bases and its applications. In: Interoperability Semantic Services and Web Applications: Emerging Concepts, ch. 5, pp. 104–130. IGI Global (2011)Google Scholar
  83. 83.
    Hellmann, S., Lehmann, J., Unbehauen, J., Stadler, C., Lam, T.N., Strohmaier, M.: Navigation-induced knowledge engineering by example. In: Takeda, H., Qu, Y., Mizoguchi, R., Kitamura, Y. (eds.) JIST 2012. LNCS, vol. 7774, pp. 207–222. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  84. 84.
    Hillner, S., Ngomo, A.-C.N.: Parallelizing limes for large-scale link discovery. In: I’Semantics (2011)Google Scholar
  85. 85.
    Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the pedantic web. In: LDOW (2010)Google Scholar
  86. 86.
    Hogan, A., Umbrich, J., Harth, A., Cyganiak, R., Polleres, A., Decker, S.: An empirical survey of linked data conformance. Journal of Web Semantics (2012)Google Scholar
  87. 87.
    Horridge, M., Patel-Schneider, P.F.: Manchester syntax for OWL 1.1. In: OWLED 2008 (2008)Google Scholar
  88. 88.
    HTML 5: A vocabulary and associated APIs for HTML and XHTML. W3C Working Draft (August 2009),
  89. 89.
    Iannone, L., Palmisano, I.: An algorithm based on counterfactuals for concept learning in the semantic web. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 370–379. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  90. 90.
    Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159 (2007)CrossRefGoogle Scholar
  91. 91.
    Inan, A., Kantarcioglu, M., Bertino, E., Scannapieco, M.: A hybrid approach to private record linkage. In: ICDE, pp. 496–505 (2008)Google Scholar
  92. 92.
    Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery without losing recall. In: WebDB (2011)Google Scholar
  93. 93.
    Isele, R., Jentzsch, A., Bizer, C.: Active learning of expressive linkage rules for the web of data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 411–418. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  94. 94.
    Jacobi, I., Kagal, L., Khandelwal, A.: Rule-based trust assessment on the semantic web. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 227–241. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  95. 95.
    Jacobs, I., Walsh, N.: Architecture of the world wide web, vol. one. World Wide Web Consortium, Recommendation REC-Webarch-20041215 (December 2004)Google Scholar
  96. 96.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345. Morgan Kaufmann (1995)Google Scholar
  97. 97.
    Juran, J.: The Quality Control Handbook. McGraw-Hill, New York (1974)Google Scholar
  98. 98.
    Kim, S.N., Kan, M.-Y.: Re-examining automatic keyphrase extraction approaches in scientific articles. In: Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications, MWE 2009, pp. 9–16. Association for Computational Linguistics, Stroudsburg (2009)CrossRefGoogle Scholar
  99. 99.
    Kim, S.N., Medelyan, O., Kan, M.-Y., Baldwin, T.: Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, pp. 21–26. Association for Computational Linguistics, Stroudsburg (2010)Google Scholar
  100. 100.
    Kittler, J., Hatef, M., Duin, R.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  101. 101.
    Kohavi, R.: The power of decision tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  102. 102.
    Köpcke, H., Thor, A., Rahm, E.: Comparative evaluation of entity resolution approaches with fever. Proc. VLDB Endow. 2(2), 1574–1577 (2009)CrossRefGoogle Scholar
  103. 103.
    Krötzsch, M., Vrandecic, D., Völkel, M., Haller, H., Studer, R.: Semantic wikipedia. Journal of Web Semantics 5, 251–261 (2007)CrossRefGoogle Scholar
  104. 104.
    Gayo, J.E.L., Kontokostas, D., Auer, S.: Multilingual linked open data patterns. Semantic Web Journal (2012)Google Scholar
  105. 105.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Machine Learning 95(1-2), 161–205 (2005)zbMATHCrossRefGoogle Scholar
  106. 106.
    le Cessie, S., van Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics 41(1), 191–201 (1992)zbMATHCrossRefGoogle Scholar
  107. 107.
    Lehmann, J.: Hybrid learning of ontology classes. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 883–898. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  108. 108.
    Lehmann, J.: DL-Learner: learning concepts in description logics. Journal of Machine Learning Research (JMLR) 10, 2639–2642 (2009)MathSciNetzbMATHGoogle Scholar
  109. 109.
    Lehmann, J.: Learning OWL Class Expressions. PhD thesis, University of Leipzig, PhD in Computer Science (2010)Google Scholar
  110. 110.
    Lehmann, J.: Ontology learning. In: Proceedings of Reasoning Web Summer School (2010)Google Scholar
  111. 111.
    Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. Journal of Web Semantics 9, 71–81 (2011)CrossRefGoogle Scholar
  112. 112.
    Lehmann, J., Bizer, C., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. Journal of Web Semantics 7(3), 154–165 (2009)CrossRefGoogle Scholar
  113. 113.
    Lehmann, J., Bühmann, L.: AutoSPARQL: Let users query your knowledge base. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 63–79. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  114. 114.
    Lehmann, J., et al.: deqa: Deep web extraction for question answering. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 131–147. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  115. 115.
    Lehmann, J., Hitzler, P.: Foundations of refinement operators for description logics. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 161–174. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  116. 116.
    Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \(\mathcal{ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  117. 117.
    Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Machine Learning Journal 78(1-2), 203–250 (2010)MathSciNetCrossRefGoogle Scholar
  118. 118.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web Journal (2014)Google Scholar
  119. 119.
    Lei, Y., Uren, V., Motta, E.: A framework for evaluating semantic metadata. In: 4th International Conference on Knowledge Capture, K-CAP 2007, vol. (8), pp. 135–142. ACM (2007)Google Scholar
  120. 120.
    Pipino, D.K.L., Wang, R., Rybold, W.: Developing Measurement Scales for Data-Quality Dimensions, vol. 1. M.E. Sharpe, New York (2005)Google Scholar
  121. 121.
    Leuf, B., Cunningham, W.: The Wiki Way: Collaboration and Sharing on the Internet. Addison-Wesley Professional (2001)Google Scholar
  122. 122.
    Lisi, F.A.: Building rules on top of ontologies for the semantic web with inductive logic programming. Theory and Practice of Logic Programming 8(3), 271–300 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  123. 123.
    Lisi, F.A., Esposito, F.: Learning SHIQ+log rules for ontology evolution. In: SWAP 2008. CEUR Workshop Proceedings, vol. 426, (2008)Google Scholar
  124. 124.
    Lohmann, S., Heim, P., Auer, S., Dietzold, S., Riechert, T.: Semantifying requirements engineering – the softwiki approach. In: Proceedings of the 4th International Conference on Semantic Technologies (I-SEMANTICS 2008), pp. 182–185. J.UCS (2008)Google Scholar
  125. 125.
    Lopez, V., Uren, V., Sabou, M.R., Motta, E.: Cross ontology query answering on the semantic web: an initial evaluation. In: K-CAP 2009, pp. 17–24. ACM, New York (2009)Google Scholar
  126. 126.
    Ma, L., Sun, X., Cao, F., Wang, C., Wang, X., Kanellos, N., Wolfson, D., Pan, Y.: Semantic enhancement for enterprise data management. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 876–892. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  127. 127.
    Martin, M., Stadler, C., Frischmuth, P., Lehmann, J.: Increasing the financial transparency of european commission project funding. Semantic Web Journal, Special Call for Linked Dataset Descriptions (2), 157–164 (2013)Google Scholar
  128. 128.
    Matsuo, Y., Ishizuka, M.: Keyword Extraction From A Single Document Using Word Co-Occurrence Statistical Information. International Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)CrossRefGoogle Scholar
  129. 129.
    Maynard, D., Peters, W., Li, Y.: Metrics for evaluation of ontology-based information extraction. In: Workshop on Evaluation of Ontologies for the Web (EON) at WWW (May 2006)Google Scholar
  130. 130.
    McBride, B., Beckett, D.: Rdf/xml syntax specification. W3C Recommendation (February 2004)Google Scholar
  131. 131.
    McCusker, J., McGuinness, D.: Towards identity in linked data. In: Proceedings of OWL Experiences and Directions Seventh Annual Workshop (2010)Google Scholar
  132. 132.
    Mecella, M., Scannapieco, M., Virgillito, A., Baldoni, R., Catarci, T., Batini, C.: Managing data quality in cooperative information systems. In: Meersman, R., Tari, Z. (eds.) CoopIS/DOA/ODBASE 2002. LNCS, vol. 2519, pp. 486–502. Springer, Heidelberg (2002)Google Scholar
  133. 133.
    Meilicke, C., Stuckenschmidt, H.: Incoherence as a basis for measuring the quality of ontology mappings. In: 3rd International Workshop on Ontology Matching (OM) at the ISWC (2008)Google Scholar
  134. 134.
    Mendes, P., Mühleisen, H., Bizer, C.: Sieve: Linked data quality assessment and fusion. In: LWDM (March 2012)Google Scholar
  135. 135.
    Mendes, P., Bizer, C., Miklos, Z., Calbimonte, J.-P., Moraru, A., Flouris, G.: D2.1: Conceptual model and best practices for high-quality metadata publishing. Technical report, PlanetData Deliverable (2012)Google Scholar
  136. 136.
    Moats, R.: Urn syntax. Internet RFC 2141 (May 1997)Google Scholar
  137. 137.
    Morsey, M., Lehmann, J., Auer, S., Ngonga Ngomo, A.-C.: DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 454–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  138. 138.
    Morsey, M., Lehmann, J., Auer, S., Ngomo, A.-C.N.: Usage-Centric Benchmarking of RDF Triple Stores. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (2012)Google Scholar
  139. 139.
    Mostafavi, M.A., Edwards, G., Jeansoulin, R.: Ontology-based method for quality assessment of spatial data bases. In: International Symposium on Spatial Data Quality, vol. 4, pp. 49–66 (2004)Google Scholar
  140. 140.
    Nadeau, D.: Semi-Supervised Named Entity Recognition: Learning to Recognize 100 Entity Types with Little Supervision. PhD thesis, University of Ottawa (2007)Google Scholar
  141. 141.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Linguisticae Investigationes 30(1), 3–26 (2007)CrossRefGoogle Scholar
  142. 142.
    Nadeau, D., Turney, P.D., Matwin, S.: Unsupervised named-entity recognition: Generating gazetteers and resolving ambiguity. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 266–277. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  143. 143.
    Naumann, F.: Quality-Driven Query Answering for Integrated Information Systems. LNCS, vol. 2261. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  144. 144.
    Ngomo, A.-C.N.: Parameter-free clustering of protein-protein interaction graphs. In: Proceedings of Symposium on Machine Learning in Systems Biology (2010)Google Scholar
  145. 145.
    Ngomo, A.-C.N.: A time-efficient hybrid approach to link discovery. In: Proceedings of OM@ISWC (2011)Google Scholar
  146. 146.
    Ngonga Ngomo, A.-C.: Link discovery with guaranteed reduction ratio in affine spaces with minkowski measures. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 378–393. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  147. 147.
    Ngomo, A.-C.N.: On link discovery using a hybrid approach. Journal on Data Semantics 1, 203–217 (2012)CrossRefGoogle Scholar
  148. 148.
    Ngomo, A.-C.N., Auer, S.: Limes - a time-efficient approach for large-scale link discovery on the web of data. In: Proceedings of IJCAI (2011)Google Scholar
  149. 149.
    Ngomo, A.-C.N., Bühmann, L., Unger, C., Lehmann, J., Gerber, D.: Sorry, i don’t speak sparql — translating sparql queries into natural language. In: Proceedings of WWW (2013)Google Scholar
  150. 150.
    Ngonga Ngomo, A.-C., Heino, N., Lyko, K., Speck, R., Kaltenböck, M.: Scms - semantifying content management systems. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 189–204. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  151. 151.
    Ngomo, A.-C.N., Kolb, L., Heino, N., Hartung, M., Auer, S., Rahm, E.: When to reach for the cloud: Using parallel hardware for link discovery. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 275–289. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  152. 152.
    Ngomo, A.-C.N., Lehmann, J., Auer, S., Höffner, K.: Raven: Active learning of link specifications. In: Proceedings of the Ontology Matching Workshop (co-located with ISWC) (2011)Google Scholar
  153. 153.
    Ngonga Ngomo, A.-C., Lyko, K.: Eagle: Efficient active learning of link specifications using genetic programming. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 149–163. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  154. 154.
    Ngomo, A.-C.N., Lyko, K., Christen, V.: Coala – correlation-aware active learning of link specifications. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 442–456. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  155. 155.
    Ngonga Ngomo, A.-C., Schumacher, F.: Border flow – a local graph clustering algorithm for natural language processing. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp. 547–558. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  156. 156.
    Nguyen, D.P.T., Matsuo, Y., Ishizuka, M.: Relation extraction from wikipedia using subtree mining. In: AAAI, pp. 1414–1420 (2007)Google Scholar
  157. 157.
    Nguyen, T., Kan, M.-Y.: Keyphrase Extraction in Scientific Publications, pp. 317–326 (2007)Google Scholar
  158. 158.
    Nienhuys-Cheng, S.-H., de Wolf, R. (eds.): Foundations of Inductive Logic Programming. LNCS, vol. 1228. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  159. 159.
    Oren, E.: SemperWiki: A Semantic Personal Wiki. In: Decker, J., Park, D., Quan, L. (eds.) roc. of Semantic Desktop Workshop at the ISWC, Galway, Ireland, vol. 175 (November 6, 2005)Google Scholar
  160. 160.
    Pantel, P., Pennacchiotti, M.: Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In: ACL, pp. 113–120. ACL Press (2006)Google Scholar
  161. 161.
    Park, Y., Byrd, R.J., Boguraev, B.K.: Automatic glossary extraction: beyond terminology identification. In: Proceedings of the 19th International Conference on Computational Linguistics, COLING 2002, vol. 1, pp. 1–7. Association for Computational Linguistics, Stroudsburg (2002)Google Scholar
  162. 162.
    Pasca, M., Lin, D., Bigham, J., Lifchits, A., Jain, A.: Organizing and searching the world wide web of facts - step one: the one-million fact extraction challenge. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 2, pp. 1400–1405. AAAI Press (2006)Google Scholar
  163. 163.
    Patel-Schneider, P.F., Hayes, P., Horrocks, I.: OWL Web Ontology Language - Semantics and Abstract Syntax. W3c:rec, W3C (February 10, 2004),
  164. 164.
    Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Communications of the ACM 45(4) (2002)Google Scholar
  165. 165.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  166. 166.
    Rahm, E.: Schema Matching and Mapping. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  167. 167.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. The VLDB Journal 10, 334–350 (2001)zbMATHCrossRefGoogle Scholar
  168. 168.
    Raimond, Y., Sutton, C., Sandler, M.: Automatic interlinking of music datasets on the semantic web. In: 1st Workshop about Linked Data on the Web (2008)Google Scholar
  169. 169.
    Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to wikipedia. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp. 1375–1384. Association for Computational Linguistics (2011)Google Scholar
  170. 170.
    Röder, M., Usbeck, R., Hellmann, S., Gerber, D., Both, A.: N3 - a collection of datasets for named entity recognition and disambiguation in the nlp interchange format. In: Language Resources and EvaluationConference, 9th edn., Reykjavik, Iceland, May 26-31 (2014)Google Scholar
  171. 171.
    Riechert, T., Lauenroth, K., Lehmann, J., Auer, S.: Towards semantic based requirements engineering. In: Proceedings of the 7th International Conference on Knowledge Management, I-KNOW (2007)Google Scholar
  172. 172.
    Riechert, T., Morgenstern, U., Auer, S., Tramp, S., Martin, M.: Knowledge engineering for historians on the example of the catalogus professorum lipsiensis. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 225–240. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  173. 173.
    Rieß, C., Heino, N., Tramp, S., Auer, S.: EvoPat – Pattern-Based Evolution and Refactoring of RDF Knowledge Bases. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 647–662. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  174. 174.
    Rudolph, S.: Exploring relational structures via FLE. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 196–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  175. 175.
    Rula, A., Palmonari, M., Harth, A., Stadtmüller, S., Maurino, A.: On the Diversity and Availability of Temporal Information in Linked Open Data. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 492–507. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  176. 176.
    Rula, A., Palmonari, M., Maurino, A.: Capturing the Age of Linked Open Data: Towards a Dataset-independent Framework. In: IEEE International Conference on Semantic Computing (2012)Google Scholar
  177. 177.
    Sahoo, S.S., Halb, W., Hellmann, S., Idehen, K., Thibodeau Jr., T., Auer, S., Sequeda, J., Ezzat, A.: A survey of current approaches for mapping of relational databases to rdf (January 2009)Google Scholar
  178. 178.
    Sampson, G.: How fully does a machine-usable dictionary cover english text. Literary and Linguistic Computing 4(1) (1989)Google Scholar
  179. 179.
    Sauermann, L., Cyganiak, R.: Cool uris for the semantic web. W3C Interest Group Note (December 2008)Google Scholar
  180. 180.
    Schaffert, S.: Ikewiki: A semantic wiki for collaborative knowledge management. In: Proceedings of the 1st International Workshop on Semantic Technologies in Collaborative Applications, STICA (2006)Google Scholar
  181. 181.
    Scharffe, F., Liu, Y., Zhou, C.: Rdf-ai: an architecture for rdf datasets matching, fusion and interlink. In: Proc. IJCAI, IR-KR Workshop (2009)Google Scholar
  182. 182.
    Sertkaya, B.: OntocomP system description. In: Grau, B.C., Horrocks, I., Motik, B., Sattler, U. (eds.) Proceedings of the 22nd International Workshop on Description Logics (DL 2009), Oxford, UK, July 27-30. CEUR Workshop Proceedings, vol. 477. (2009)Google Scholar
  183. 183.
    Settles, B.: Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers (2012)Google Scholar
  184. 184.
    Shekarpour, S., Katebi, S.D.: Modeling and evaluation of trust with an extension in semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 8(1), 26–36 (2010)CrossRefGoogle Scholar
  185. 185.
    Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. Technical report (August 01, 2008)Google Scholar
  186. 186.
    Souzis, A.: Building a Semantic Wiki. IEEE Intelligent Systems 20(5), 87–91 (2005)CrossRefGoogle Scholar
  187. 187.
    Spanos, D.-E., Stavrou, P., Mitrou, N.: Bringing relational databases into the semantic web: A survey. Semantic Web 3(2), 169–209 (2012)Google Scholar
  188. 188.
    Stadler, C., Lehmann, J., Höffner, K., Auer, S.: Linkedgeodata: A core for a web of spatial open data. Semantic Web Journal 3(4), 333–354 (2012)Google Scholar
  189. 189.
    Thielen, C.: An approach to proper name tagging for german. In: Proceedings of the EACL-95 SIGDAT Workshop (1995)Google Scholar
  190. 190.
    Tramp, S., Frischmuth, P., Ermilov, T., Auer, S.: Weaving a Social Data Web with Semantic Pingback. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS (LNAI), vol. 6317, pp. 135–149. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  191. 191.
    Tramp, S., Heino, N., Auer, S., Frischmuth, P.: RDFauthor: Employing RDFa for collaborative Knowledge Engineering. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS (LNAI), vol. 6317, pp. 90–104. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  192. 192.
    Peter, D.: Turney. Coherent keyphrase extraction via web mining. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 434–439. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  193. 193.
    Unger, C., Bühmann, L., Lehmann, J., Ngomo, A.-C.N., Gerber, D., Cimiano, P.: Template-based question answering over rdf data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 639–648 (2012)Google Scholar
  194. 194.
    Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., Bal, H.: Owl reasoning with webpie: calculating the closure of 100 billion triples. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  195. 195.
    Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  196. 196.
    Völker, J., Rudolph, S.: Fostering web intelligence by semi-automatic OWL ontology refinement. In: Web Intelligence, pp. 454–460. IEEE (2008)Google Scholar
  197. 197.
    Völker, J., Vrandečić, D., Sure, Y., Hotho, A.: Learning disjointness. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 175–189. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  198. 198.
    Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and maintaining links on the web of data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 650–665. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  199. 199.
    Walker, D., Amsler, R.: The use of machine-readable dictionaries in sublanguage analysis. In: Analysing Language in Restricted Domains (1986)Google Scholar
  200. 200.
    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Communications of the ACM 39(11), 86–95 (1996)CrossRefGoogle Scholar
  201. 201.
    Wang, G., Yu, Y., Zhu, H.: Pore: Positive-only relation extraction from wikipedia text. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 580–594. Springer, Heidelberg (2007)Google Scholar
  202. 202.
    Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems 12(4), 5–33 (1996)CrossRefGoogle Scholar
  203. 203.
    Watanabe, H., Muggleton, S.: Can ILP be applied to large dataset? In: De Raedt, L. (ed.) ILP 2009. LNCS (LNAI), vol. 5989, pp. 249–256. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  204. 204.
    Winkler, W.: The state of record linkage and current research problems. Technical report, Statistical Research Division, U.S. Bureau of the Census (1999)Google Scholar
  205. 205.
    Winkler, W.: Overview of record linkage and current research directions. Technical report, Bureau of the Census - Research Report Series (2006)Google Scholar
  206. 206.
    Wu, D., Ngai, G., Carpuat, M.: A stacked, voted, stacked model for named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, CONLL 2003, vol. 4, pp. 200–203. Association for Computational Linguistics, Stroudsburg (2003)CrossRefGoogle Scholar
  207. 207.
    Wu, H., Zubair, M., Maly, K.: Harvesting social knowledge from folksonomies. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, HYPERTEXT 2006, pp. 111–114. ACM, New York (2006)CrossRefGoogle Scholar
  208. 208.
    Yan, Y., Okazaki, N., Matsuo, Y., Yang, Z., Ishizuka, M.: Unsupervised relation extraction by mining wikipedia texts using information from the web. In: ACL 2009, pp. 1021–1029 (2009)Google Scholar
  209. 209.
    Yu, Y., Heflin, J.: Extending functional dependency to detect abnormal data in RDF Graphs. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 794–809. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  210. 210.
    Zaveri, A., Kontokostas, D., Sherif, M.A., Bühmann, L., Morsey, M., Auer, S., Lehmann, J.: User-driven quality evaluation of dbpedia. To appear in Proceedings of 9th International Conference on Semantic Systems, I-SEMANTICS 2013, Graz, Austria, September 4-6, pp. 97–104. ACM (2013)Google Scholar
  211. 211.
    Zaveri, A., Lehmann, J., Auer, S., Hassan, M.M., Sherif, M.A., Martin, M.: Publishing and interlinking the global health observatory dataset. Semantic Web Journal, Special Call for Linked Dataset Descriptions (3), 315–322 (2013)Google Scholar
  212. 212.
    Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment methodologies for linked open data. Under review,
  213. 213.
    Zhou, G., Su, J.: Named entity recognition using an hmm-based chunk tagger. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, pp. 473–480. Association for Computational Linguistics, Morristown (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Sören Auer
    • 1
  • Jens Lehmann
    • 1
  • Amrapali Zaveri
    • 1
  1. 1.AKSW, Institut für InformatikUniversität LeipzigLeipzigGermany

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