Modelling OWL Ontologies with Graffoo

Falco, R Vanacore; Gangemi, Aldo; Peroni, Silvio; Shotton, David M.; Vitali, Fabio

doi:10.1007/978-3-319-11955-7_42

Cited by 55 publications

(29 citation statements)

References 4 publications

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“…The main concepts and interrelations of the model are shown in Fig. 2 (modelled with Graffoo [17]). We note that both the domain of dpv:hasPurpose and the range of dpv:hasDataSubject is the union of dpv:Consent and dpv:PersonalDataHandling.…”

Section: Representing Consent Informationmentioning

confidence: 99%

“…We test for the presence of a csvw:propertyURL that refers to an RDF predicate. As the use of csvw:propertyURL is not mandatory, we will construct a predicate based on the column's name from the schema when that property is missing (lines [17][18][19]. We note that a base URI-usually the URL or IRI of the dataset-is needed to provide absolute URIs of the column names.…”

Section: Step 2: Generation Of An R2rml Mappingmentioning

confidence: 99%

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“Just-in-time” generation of datasets by considering structured representations of given consent for GDPR compliance

et al. 2020

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Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one's permission for using one's personal information for specific data processing purposes. Organizations must demonstrate that they comply with these policies. The fines that come with non-compliance are of such importance that it has driven research in facilitating compliance verification. The state-of-the-art primarily focuses on, for instance, the analysis of prescriptive models and posthoc analysis on logs to check whether data processing is compliant to GDPR. We argue that GDPR compliance can be facilitated by ensuring datasets used in processing activities are compliant with consent from the very start. The problem addressed in this paper is how we can generate datasets that comply with given consent "just-in-time". We propose RDF and OWL ontologies to represent the consent that an organization has collected and its relationship with data processing purposes. We use this ontology to annotate schemas, allowing us to generate declarative mappings that transform (relational) data into RDF driven by the annotations. We furthermore demonstrate how we can create compliant datasets by altering the results of the mapping. The use of RDF and OWL allows us to implement the entire process in a declarative manner using SPARQL. We have integrated all components in a service that furthermore captures provenance information for each step, further contributing to the transparency that is needed towards facilitating compliance verification. We demonstrate the approach with a synthetic dataset simulating users (re-)giving, withdrawing, and rejecting their consent on data processing purposes of systems. In summary, it is argued that B Christophe Debruyne debruync@tcd.ie Harshvardhan J. Pandit 123 3616 C. Debruyne et al.the approach facilitates transparency and compliance verification from the start, reducing the need for posthoc compliance analysis common in the state-of-the-art.

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Section: Representing Consent Informationmentioning

confidence: 99%

Section: Step 2: Generation Of An R2rml Mappingmentioning

confidence: 99%

“Just-in-time” generation of datasets by considering structured representations of given consent for GDPR compliance

et al. 2020

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“…• the attributes for citations shown in Table 1 Followed the first release of COCI on June 4, 2018, the most recent version of COCI, released on November 12, 2018, contains more that 445 million DOI-to-DOI citations that are included in the open and the limited datasets of Crossref reference data 3 . All the citation data in COCI and their provenance information, described according the Graffoo diagram (Falco et al 2014) presented in Fig. 2, are released under a CC0 waiver, and are compliant with the FAIR data principles (Wilkinson et al 2016).…”

Section: Citation Timespanmentioning

confidence: 99%

Software review: COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations

2019

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In this paper, we present COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations (http://opencitations.net/index/coci). COCI is the first open citation index created by OpenCitations, in which we have applied the concept of citations as first-class data entities, and it contains more than 445 million DOI-to-DOI citation links derived from the data available in Crossref. These citations are described using the Resource Description Framework (RDF) by means of the newly extended version of the OpenCitations Data Model (OCDM). We introduce the workflow we have developed for creating these data, and also show the additional services that facilitate the access to and querying of these data via different access points: a SPARQL endpoint, a REST API, bulk downloads, Web interfaces, and direct access to the citations via HTTP content negotiation. Finally, we present statistics regarding the use of COCI citation data, and we introduce several projects that have already started to use COCI data for different purposes. Article Highlights• COCI contains more than 445 million DOI-to-DOI citation links made available under a CC0 public domain waiver• COCI uses an alternative richer view that regards citations as first-class data entities with accompanying properties• Citation data in COCI can be accessed in a variety of ways including SPARQL endpoint, REST API, interfaces, and dumps

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“…The current version of F Entry Ontology (FEO) revises the previous version introduced in [13], and it is available online at http://www.essepuntato.it/2014/03/fentry. The Graffoo diagram [10] of FEO in Illustration 1 provides an overview of its classes -i.e. the yellow rectangles -, object properties -i.e.…”

Section: The F Entry Ontology (Feo)mentioning

confidence: 99%

Enhancing Semantic Expressivity in the Cultural Heritage Domain

Daquino

Mambelli

Peroni

et al. 2017

J. Comput. Cult. Herit.

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Describing cultural heritage objects from the perspective of Linked Open Data (LOD) is not a trivial task. The process often requires not only choosing pertinent ontologies, but also developing new models that preserve the most information and express the semantic power of cultural heritage data. Indeed, data managed in archives, libraries and museums are complex objects themselves, which require a deep reflection on even non-conventional conceptual models. Starting from these considerations, this paper describes a research project: to expose the vastness of one of the most important collections of European cultural heritage, the Zeri Photo Archive, as Linked Open Data. We describe here the steps we undertook to this end: firstly, we developed two ad hoc ontologies for describing all the issues not completely covered by existent models (the F Entry and the OA Entry Ontology); then we mapped into RDF the descriptive elements used in the current Zeri Photo Archive catalog, converting into CIDOC-CRM and into the two new aforementioned models the source data based on the Italian content standards Scheda F (Photography Entry, in English) and Scheda OA (Work of Art Entry, in English); and finally, we created an RDF dataset of the output of the mapping that could show a result capable of demonstrating the complexity of our scenario.

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Modelling OWL Ontologies with Graffoo

Cited by 55 publications

References 4 publications

“Just-in-time” generation of datasets by considering structured representations of given consent for GDPR compliance

“Just-in-time” generation of datasets by considering structured representations of given consent for GDPR compliance

Software review: COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations

Enhancing Semantic Expressivity in the Cultural Heritage Domain

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