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Linked Data Cleansing and Change Management

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Knowledge Engineering and Knowledge Management (EKAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8982))

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Abstract

The Web of Data is constantly growing in terms of covered domains, applied vocabularies, and number of triples. A high level of data quality is in the best interest of any data consumer.

Linked Data publishers can use various data quality evaluation tools prior to publication of their datasets. But nevertheless, most inconsistencies only become obvious when the data is processed in applications and presented to the end users. Therefore, it is not only the responsibility of the original data publishers to keep their data tidy, but progresses to become a mission for all distributors and consumers of Linked Data, too.

My main research topic is the inspection of feedback mechanisms for Linked Data cleansing in open knowledge bases. This work includes a change request vocabulary, the aggregation of change requests produced by various agents, versioning data resources, and consumer notification about changes. The individual components form the basis of a Linked Data Change Management framework.

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References

  1. Arnold, S.E.: Information manufacturing: The road to database quality. Database 15(5), 32–39 (1992)

    Google Scholar 

  2. Auer, S., Bühmann, L., Dirschl, C., Erling, O., Hausenblas, M., Isele, R., Lehmann, J., Martin, M., Mendes, P.N., van Nuffelen, B., Stadler, C., Tramp, S., Williams, H.: Managing the life-cycle of linked data with the LOD2 stack. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 1–16. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  3. Auer, S., Demter, J., Martin, M., Lehmann, J.: LODStats – an extensible framework for high-performance dataset analytics. 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. 353–362. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 154–165 (2009)

    Article  Google Scholar 

  5. de Sompel, H.V., Sanderson, R., Nelson, M.L., Balakireva, L., Shankar, H., Ainsworth, S.: An HTTP-based versioning mechanism for linked data. In: LDOW2010, Raleigh, USA, April 2010

    Google Scholar 

  6. Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the pedantic web. In: LDOW 2010. CEUR Workshop Proceedings. vol. 628 (2010)

    Google Scholar 

  7. Juran, J., Godfrey, A.: Juran’s quality handbook. Juran’s quality handbook, 5e. McGraw Hill (1999)

    Google Scholar 

  8. Knuth, M., Hercher, J., Sack, H.: Collaboratively patching linked data. In: USEWOD 2012, Lyon, France, April 2012

    Google Scholar 

  9. Knuth, M., Sack, H.: Data cleansing consolidation with PatchR. In: ESWC2014, Best Poster Award, May 2014

    Google Scholar 

  10. Kontokostas, D., Westphal, P., Auer, S., Hellmann, S., Lehmann, J., Cornelissen, R., Zaveri, A.: Test-driven evaluation of linked data quality. In: WWW2014 (2014)

    Google Scholar 

  11. Kontokostas, D., Zaveri, A., Auer, S., Lehmann, J.: TripleCheckMate: a tool for crowdsourcing the quality assessment of linked data. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2013. CCIS, vol. 394, pp. 265–272. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Orr, K.: Data quality and systems theory. Communications of the ACM 41(2), 66–71 (1998)

    Article  MathSciNet  Google Scholar 

  13. Paulheim, H., Bizer, C.: Type inference on noisy RDF data. 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 I. LNCS, vol. 8218, pp. 510–525. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Singh, R., Singh, K.: A descriptive classification of causes of data quality problems in data warehousing. International Journal of Computer Science Issues 7(3), 41–50 (2010)

    Google Scholar 

  15. Waitelonis, J., Ludwig, N., Knuth, M., Sack, H.: WhoKnows? - evaluating linked data heuristics with a quiz that cleans up DBpedia. International Journal of Interactive Technology and Smart Education (ITSE) 8(3), 236–248 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Magnus Knuth

Authors
  1. Magnus Knuth
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Corresponding author

Correspondence to Magnus Knuth .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Patrick Lambrix

  2. Aalto University, Espoo, Finland

    Eero Hyvönen

  3. Linköping University, Linköping, Sweden

    Eva Blomqvist

  4. STLab ISTC-CNR, Rome, Italy

    Valentina Presutti

  5. Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  6. University of Manchester, Manchester, United Kingdom

    Uli Sattler

  7. Indiana University Bloomington, Bloomington, Indiana, USA

    Ying Ding

  8. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

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© 2015 Springer International Publishing Switzerland

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Knuth, M. (2015). Linked Data Cleansing and Change Management. In: Lambrix, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8982. Springer, Cham. https://doi.org/10.1007/978-3-319-17966-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-17966-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17965-0

  • Online ISBN: 978-3-319-17966-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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