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Hybrid Refining Approach of PrOnto Ontology

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Electronic Government and the Information Systems Perspective (EGOVIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12394))

Abstract

This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction (OKE) algorithm. To ensure robustness of the results while preserving an interdisciplinary approach, the integration of legal and technical knowledge has been carried out as follows. The set of privacy policies was first analysed by the legal experts to discover legal concepts and map the text into PrOnto. The mapping was then provided to computer scientists to perform the OKE analysis. Results were validated by the legal experts, who provided feedbacks and refinements (i.e. new classes and modules) of the ontology according to MeLOn methodology. Three iterations were performed on a set of (development) policies, and a final test using a new set of privacy policies. The results are 75,43% of detection of concepts in the policy texts and an increase of roughly 33% in the accuracy gain on the test set, using the new refined version of PrOnto enriched with SKOS-XL lexicon terms and definitions.

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Notes

  1. 1.

    https://www.w3.org/TR/sparql11-query/, last accessed 2020/06/19.

  2. 2.

    https://www.w3.org/TR/rdf11-concepts/, last accessed 2020/06/19.

  3. 3.

    https://www.w3.org/TR/skos-reference/skos-xl.html, last accessed 2020/06/19.

  4. 4.

    PrOnto reuses existing ontologies ALLOT [4] FRBR [19], LKIF [6] we use in particular lkif:Agent to model lkif:Organization, lkif:Person and lkif:Role [6], the Publishing Workflow Ontology (PWO) [13], Time-indexed Value in Context (TVC) and Time Interval [30]. Now with this work we include also SKOS-XL [5, 8].

  5. 5.

    Rover, Parkclick, Springer, Zalando, Louis Vuitton, Burger King, Microsoft-Skype, Lufthansa, Booking, Zurich Insurance.

  6. 6.

    https://spacy.io, last accessed 2020/06/19.

  7. 7.

    https://gitlab.com/CIRSFID/un-challange-2019, last accessed 2020/06/19.

  8. 8.

    https://www.betterinternetforkids.eu/web/portal/practice/awareness/detail?articleId=3017751, last accessed 2020/06/19.

  9. 9.

    COM (2019) 250 final “data which were initially personal data, but were later made anonymous. The ‘anonymisation’ of personal data is different to pseudonymisation (see above), as properly anonymised data cannot be attributed to a specific person, not even by use of additional data and are therefore non-personal data”.

  10. 10.

    Recital 26 GDPR “5. The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. 6. This Regulation does not therefore concern the processing of such anonymous information, including for statistical or research purposes”.

  11. 11.

    https://www.specialprivacy.eu/, last accessed 2020/06/19.

  12. 12.

    https://publications.europa.eu/en/web/eu-vocabularies/th-dataset/-/resource/dataset/eurovoc, last accessed 2020/06/19.

  13. 13.

    https://iate.europa.eu/, last accessed 2020/06/19.

  14. 14.

    https://www.w3.org/TR/skos-reference/skos-xl.html, last accessed 2020/06/19.

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Acknowledgements

This work was partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690974 “MIREL: MIning and REasoning with Legal texts”.

Author information

Authors and Affiliations

  1. CIRSFID, University of Bologna, Bologna, Italy

    Monica Palmirani, Giorgia Bincoletto & Salvatore Sapienza

  2. Computer Science Department, University of Turin, Turin, Italy

    Valentina Leone

  3. DISI, University of Bologna, Bologna, Italy

    Francesco Sovrano

Authors
  1. Monica Palmirani
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  2. Giorgia Bincoletto
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  3. Valentina Leone
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  4. Salvatore Sapienza
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  5. Francesco Sovrano
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Corresponding author

Correspondence to Monica Palmirani .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  2. National Research Council, Florence, Italy

    Enrico Francesconi

  3. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Ismail Khalil

Appendix

Appendix

In this section we will provide additional data (technical results and measurements) resulting from the experiments described in this paper. More precisely, we present the statistics obtained from the experiments on both the first and the second set of privacy policies.

First Set of Privacy Policies (Development Set)

PrOnto version

SKOS support

Found ontology concepts

Ontology concepts

Presence

Accuracy gain

8

No

87

139

62,65%

0%

9

No

111

172

64,91%

27,58%

9

Yes

123

172

71,92%

41,37%

Second Set of Privacy Policies (Test Set)

PrOnto version

SKOS support

Found ontology concepts

Ontology concepts

Presence

Accuracy gain

8

No

97

139

69,78%

0%

9

No

119

172

69,59%

22,68%

9

Yes

129

172

75,43%

32,98%

Where:

  • The “Accuracy Gain” is computed as (x2 - x1)/x1, where x1 is the number of “Found Ontology Concepts” with PrOnto v8 without SKOS support and x2 is the number of “Found Ontology Concepts” of any of the other versions of PrOnto.

  • The “Presence” is computed as the ratio of “Found Ontology Concepts” and “Ontology Concepts”.

  • “Ontology Concepts” is the total number of concepts in the ontology.

  • “Found Ontology Concepts” is the number of concepts of the ontology that have been identified by the OKE tool in the set of policies. “Found Ontology Concepts” is always lower than “Ontology Concepts”.

  • “SKOS Support” is a boolean indicating whether it has been used SKOS support or not.

  • “PrOnto Version” indicates the version of PrOnto.

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Palmirani, M., Bincoletto, G., Leone, V., Sapienza, S., Sovrano, F. (2020). Hybrid Refining Approach of PrOnto Ontology. In: Kő, A., Francesconi, E., Kotsis, G., Tjoa, A., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2020. Lecture Notes in Computer Science(), vol 12394. Springer, Cham. https://doi.org/10.1007/978-3-030-58957-8_1

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