Skip to main content
SpringerLink
Log in

A Self-Adaptive and Incremental Approach for Data Profiling in the Semantic Web

  • Chapter
  • First Online:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIX

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 10120))

Abstract

The increasing adoption of linked data principles has led to the availability of a huge amount of datasets on the Web. However, the use of these datasets is hindered by the lack of descriptive information about their content. Indeed, interlinking, matching or querying them requires some knowledge about the types and properties they contain.

In this paper, we tackle the problem of describing the content of an RDF dataset by profiling its entities, which consists in discovering the implicit types and providing their description. Each type is described by a profile composed of properties and their probabilities. Our approach relies on a clustering algorithm. It is self-adaptive, as it can automatically detect the most appropriate similarity threshold according to the dataset. Our algorithms generate overlapping clusters, enabling the detection of several types for an entity. As a dataset may evolve, our approach is incremental and can assign a type to a new entity and update the type profiles without browsing the whole dataset. We also present some experimental evaluations to demonstrate the effectiveness of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Resource Description Framework: http://www.w3.org/RDF/.

  2. 2.

    RDF Schema: http://www.w3.org/TR/rdf-schema/.

  3. 3.

    Web Ontology Language: http://www.w3.org/OWL/.

  4. 4.

    Knofuss: technologies.kmi.open.ac.uk/knofuss.

  5. 5.

    Silk: wifo5-03.informatik.uni-mannheim.de/bizer/silk.

  6. 6.

    Conference: data.semanticweb.org/dumps/conferences/dc-2010-complete.rdf.

  7. 7.

    BNF: datahub.io/fr/dataset/data-bnf-fr.

  8. 8.

    DBpedia: dbpedia.org.

  9. 9.

    http://datahub.io/group/lodcloud.

  10. 10.

    http://vocab.deri.ie/void.

References

  1. The World Wide Web Consortium (w3c) - RDF 1.1 concepts and abstract syntax. https://www.w3.org/TR/rdf11-concepts/

  2. Abedjan, Z., Gruetze, T., Jentzsch, A., Naumann, F.: Profiling and mining RDF data with ProLOD++. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 1198–1201. IEEE (2014)

    Google Scholar 

  3. Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. ACM Sigmod Record 28, 49–60 (1999). ACM

    Article  Google Scholar 

  4. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A nucleus for a web of open data the semantic web. The Semantic Web (2007)

    Google Scholar 

  5. Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. ACM SIGMOD Record 19, 322–331 (1990). ACM

    Article  Google Scholar 

  6. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy C-Means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)

    Article  Google Scholar 

  7. Böhm, C., Lorey, J., Naumann, F.: Creating void descriptions for web-scale data. Web Semant. Sci. Serv. Agents World Wide Web 9(3), 339–345 (2011)

    Article  Google Scholar 

  8. Böhm, C., Naumann, F., Abedjan, Z., Fenz, D., Grütze, T., Hefenbrock, D., Pohl, M., Sonnabend, D.: Profiling linked open data with ProLOD. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), pp. 175–178. IEEE (2010)

    Google Scholar 

  9. Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: SPARQL web-querying infrastructure: ready for action? In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41338-4_18

    Chapter  Google Scholar 

  10. Christodoulou, K., Paton, N.W., Fernandes, A.A.A.: Structure inference for linked data sources using clustering. In: Hameurlain, A., Küng, J., Wagner, R., Bianchini, D., Antonellis, V., Virgilio, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX. LNCS, vol. 8990, pp. 1–25. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46562-2_1

    Google Scholar 

  11. Clerkin, P., Cunningham, P., Hayes, C.: Ontology discovery for the semantic web using hierarchical clustering. Semantic Web Min. 1, 17 (2001)

    Google Scholar 

  12. Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques (1991)

    Google Scholar 

  13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proceedings of the 24th International Conference on Very Large Data Bases, pp. 323–333. Morgan Kaufmann Publishers Inc. (1998)

    Google Scholar 

  15. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)

    Google Scholar 

  16. Fanizzi, N., Amato, C.D., Esposito, F.: Metric-based stochastic conceptual clustering for ontologies. Inf. Syst. 34(8), 792–806 (2009)

    Article  Google Scholar 

  17. Fetahu, B., Dietze, S., Pereira Nunes, B., Antonio Casanova, M., Taibi, D., Nejdl, W.: A scalable approach for efficiently generating structured dataset topic profiles. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 519–534. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07443-6_35

    Chapter  Google Scholar 

  18. Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2(2), 139–172 (1987)

    Google Scholar 

  19. Heath, T., Bizer, C.: Linked data: Evolving the web into a global data space. Synth. Lect. Semant. Web Theor. Technol. 1(1), 1–136 (2011)

    Article  Google Scholar 

  20. Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Article  Google Scholar 

  21. Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. Reports of the Faculty of Mathematics and Informatics, Faculty of Mathematics and Informatics (1987)

    Google Scholar 

  22. Kellou-Menouer, K., Kedad, Z.: Evaluating the gap between an RDF dataset and its schema. In: Jeusfeld, M.A., Karlapalem, K. (eds.) ER 2015. LNCS, vol. 9382, pp. 283–292. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25747-1_28

    Chapter  Google Scholar 

  23. Kellou-Menouer, K., Kedad, Z.: Schema discovery in RDF data sources. In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., López, Ó.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 481–495. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25264-3_36

    Chapter  Google Scholar 

  24. Konrath, M., Gottron, T., Staab, S., Scherp, A.: Schemex: efficient construction of a data catalogue by stream-based indexing of linked data. Web Semant. Sci. Serv. Agents on the World Wide Web 16, 52–58 (2012)

    Article  Google Scholar 

  25. Li, H.: Data profiling for semantic web data. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds.) WISM 2012. LNCS, vol. 7529, pp. 472–479. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33469-6_59

    Chapter  Google Scholar 

  26. Maali, F., Campinas, S., Decker, S.: Gagg: a graph aggregation operator. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 491–504. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18818-8_30

    Chapter  Google Scholar 

  27. Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer Science & Business Media (2013)

    Google Scholar 

  28. Naumann, F.: Data profiling revisited. ACM SIGMOD Record 42(4), 40–49 (2014)

    Article  Google Scholar 

  29. Nestorov, S., Abiteboul, S., Motwani, R.: Extracting schema from semistructured data. ACM SIGMOD Record 27, 295–306 (1998). ACM

    Article  Google Scholar 

  30. Pena, P.: Determining the similarity threshold for clustering algorithms in the logical combinatorial pattern recognition through a dendograme. In: 4th Iberoamerican Simposium of Pattern Recognition, pp. 259–265 (1999)

    Google Scholar 

  31. Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68234-9_39

    Chapter  Google Scholar 

  32. Reyes-Gonzalez, R., Ruiz-Shulcloper, J.: An algorithm for restricted structuralization of spaces. Proc. IV SIARP 267 (1999)

    Google Scholar 

  33. Rizzo, G., Fanizzi, N., d’Amato, C., Esposito, F.: Prediction of class and property assertions on OWL ontologies through evidence combination. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics, p. 45. ACM (2011)

    Google Scholar 

  34. Sánchez-Díaz, G., Martínez-Trinidad, J.F.: Determination of similarity threshold in clustering problems for large data sets. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 611–618. Springer, Heidelberg (2003). doi:10.1007/978-3-540-24586-5_75

    Chapter  Google Scholar 

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

    Google Scholar 

  36. Wang, Q.Y., Yu, J.X., Wong, K.-F.: Approximate graph schema extraction for semi-structured data. In: Zaniolo, C., Lockemann, P.C., Scholl, M.H., Grust, T. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 302–316. Springer, Heidelberg (2000). doi:10.1007/3-540-46439-5_21

    Chapter  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the French National Research Agency through the CAIR ANR-14-CE23-0006 project.

Author information

Authors and Affiliations

  1. DAVID Laboratory - University of Versailles Saint-Quentin-en-Yvelines, Versailles, France

    Kenza Kellou-Menouer & Zoubida Kedad

Authors
  1. Kenza Kellou-Menouer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zoubida Kedad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenza Kellou-Menouer .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag GmbH Germany

About this chapter

Cite this chapter

Kellou-Menouer, K., Kedad, Z. (2016). A Self-Adaptive and Incremental Approach for Data Profiling in the Semantic Web. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIX. Lecture Notes in Computer Science(), vol 10120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54037-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-54037-4_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-54036-7

  • Online ISBN: 978-3-662-54037-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation