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Automatic discovery of high-level provenance using semantic similarity

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Abstract
As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision. Lastly, we discuss possible improvements and future work.
Keywords
Similarity, Semantic Web, Linked Data, Provenance, News, Data Model

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Chicago
De Nies, Tom, Sam Coppens, Davy Van Deursen, Erik Mannens, and Rik Van de Walle. 2012. “Automatic Discovery of High-level Provenance Using Semantic Similarity.” In Lecture Notes in Computer Science, ed. P Groth and J Frew, 7525:97–110. Springer.
APA
De Nies, T., Coppens, S., Van Deursen, D., Mannens, E., & Van de Walle, R. (2012). Automatic discovery of high-level provenance using semantic similarity. In P. Groth & J. Frew (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 7525, pp. 97–110). Presented at the 4th International Provenance and Annotation Workshop (IPAW - 2012), Springer.
Vancouver
1.
De Nies T, Coppens S, Van Deursen D, Mannens E, Van de Walle R. Automatic discovery of high-level provenance using semantic similarity. In: Groth P, Frew J, editors. LECTURE NOTES IN COMPUTER SCIENCE. Springer; 2012. p. 97–110.
MLA
De Nies, Tom, Sam Coppens, Davy Van Deursen, et al. “Automatic Discovery of High-level Provenance Using Semantic Similarity.” Lecture Notes in Computer Science. Ed. P Groth & J Frew. Vol. 7525. Springer, 2012. 97–110. Print.
@inproceedings{3232929,
  abstract     = {As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73\% of the original sources of 410 news stories, at 68\% precision. Lastly, we discuss possible improvements and future work.},
  author       = {De Nies, Tom and Coppens, Sam and Van Deursen, Davy and Mannens, Erik and Van de Walle, Rik},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {Groth, P and Frew, J},
  isbn         = {9783642342226},
  issn         = {0302-9743},
  keyword      = {Similarity,Semantic Web,Linked Data,Provenance,News,Data Model},
  language     = {eng},
  location     = {Santa Barbara, California},
  pages        = {97--110},
  publisher    = {Springer},
  title        = {Automatic discovery of high-level provenance using semantic similarity},
  url          = {http://dx.doi.org/10.1007/978-3-642-34222-6\_8},
  volume       = {7525},
  year         = {2012},
}

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