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Assessing and refining mappings to RDF to improve dataset quality

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Abstract
RDF dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the RDF dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for RDF datasets stemming originally from (semi-) structured data (e.g., CSV, XML, JSON). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the RDF dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as DBpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an RDF dataset in the observed cases.
Keywords
SPARQL, R2RML, RDFUNIT, Linked data mapping, RML, Data quality

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MLA
Dimou, Anastasia et al. “Assessing and Refining Mappings to RDF to Improve Dataset Quality.” Lecture Notes in Computer Science. Vol. 9367. CHAM: SPRINGER INT PUBLISHING AG, 2015. 133–149. Print.
APA
Dimou, A., Kontokostas, D., Freudenberg, M., Verborgh, R., Lehmann, J., Mannens, E., Hellmann, S., et al. (2015). Assessing and refining mappings to RDF to improve dataset quality. Lecture Notes in Computer Science (Vol. 9367, pp. 133–149). Presented at the 14th International Semantic Web Conference (ISWC), CHAM: SPRINGER INT PUBLISHING AG.
Chicago author-date
Dimou, Anastasia, Dimitirs Kontokostas, Markus Freudenberg, Ruben Verborgh, Jens Lehmann, Erik Mannens, Sebastian Hellmann, and Rik Van de Walle. 2015. “Assessing and Refining Mappings to RDF to Improve Dataset Quality.” In Lecture Notes in Computer Science, 9367:133–149. CHAM: SPRINGER INT PUBLISHING AG.
Chicago author-date (all authors)
Dimou, Anastasia, Dimitirs Kontokostas, Markus Freudenberg, Ruben Verborgh, Jens Lehmann, Erik Mannens, Sebastian Hellmann, and Rik Van de Walle. 2015. “Assessing and Refining Mappings to RDF to Improve Dataset Quality.” In Lecture Notes in Computer Science, 9367:133–149. CHAM: SPRINGER INT PUBLISHING AG.
Vancouver
1.
Dimou A, Kontokostas D, Freudenberg M, Verborgh R, Lehmann J, Mannens E, et al. Assessing and refining mappings to RDF to improve dataset quality. Lecture Notes in Computer Science. CHAM: SPRINGER INT PUBLISHING AG; 2015. p. 133–49.
IEEE
[1]
A. Dimou et al., “Assessing and refining mappings to RDF to improve dataset quality,” in Lecture Notes in Computer Science, Bethlehem, PA, 2015, vol. 9367, pp. 133–149.
@inproceedings{8030828,
  abstract     = {RDF dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the RDF dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for RDF datasets stemming originally from (semi-) structured data (e.g., CSV, XML, JSON). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the RDF dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as DBpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an RDF dataset in the observed cases.},
  author       = {Dimou, Anastasia and Kontokostas, Dimitirs and Freudenberg, Markus and Verborgh, Ruben and Lehmann, Jens and Mannens, Erik and Hellmann, Sebastian and Van de Walle, Rik},
  booktitle    = {Lecture Notes in Computer Science},
  isbn         = {978-3-319-25009-0},
  issn         = {0302-9743},
  keywords     = {SPARQL,R2RML,RDFUNIT,Linked data mapping,RML,Data quality},
  language     = {eng},
  location     = {Bethlehem, PA},
  pages        = {133--149},
  publisher    = {SPRINGER INT PUBLISHING AG},
  title        = {Assessing and refining mappings to RDF to improve dataset quality},
  url          = {http://dx.doi.org/10.1007/978-3-319-25010-6_8},
  volume       = {9367},
  year         = {2015},
}

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