Abstract The Relational to RDF Mapping Language (R2RML) became a W3C Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond relational databases was swiftly explored. As a result, several extensions and new mapping languages were proposed to tackle the limitations that surfaced as R2RML was applied in real-world use cases. Over the years, one of these languages, the RDF Mapping Language (RML), has gathered a large community of contributors, users, and compliant tools. So far, there has been no well-defined set of features for the mapping language, nor was there a consensus-marking ontology. Consequently, it has become challenging for non-experts to fully comprehend and utilize the full range of the language’s capabilities. After three years of work, the W3C Community Group on Knowledge Graph Construction proposes a new specification for RML. This paper presents the new modular RML ontology and the accompanying SHACL shapes that complement the specification. We discuss the motivations and challenges that emerged when extending R2RML, the methodology we followed to design the new ontology while ensuring its backward compatibility with R2RML, and the novel features which increase its expressiveness. The new ontology consolidates the potential of RML, empowers practitioners to define mapping rules for constructing RDF graphs that were previously unattainable, and allows developers to implement systems in adherence with [R2]RML. Resource type: Ontology/License: CC BY 4.0 International DOI: 10.5281/zenodo.7918478 /URL: http://w3id.org/rml/portal/

Data integration is the process of selecting, preprocessing, and transforming data from heterogeneous sources in data-driven projects. This process also requires the most time, effort, resources. Data integration is such an involved process due to the many informed decisions one has to make. These decisions are influenced by the complex context of a data-driven project. We argue that using said context could facilitate the decision-making processes and even automate some integration steps. However, the problem we identify in this paper is that the context of a data-driven project is tacit and, therefore, not easily accessible by humans and certainly not by software agents. From the SotA, however, we observe that current models represent the context in crude and simplistic terms. These context models are furthermore built for specific tasks or application domains such as query optimization or a smart home. The current state of affairs is thus is not fit for intelligent data integration. Next to identifying the problem, we postulate that solving this problem requires two steps: formalizing context and using that context for building context-aware agents. We illustrate this notion of "context-aware data integration" with preliminary results obtained with a use case in the domain of GDPR, more specifically the generation of datasets that takes into account informed consent.

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 the approach facilitates transparency and compliance verification from the start, reducing the need for posthoc compliance analysis common in the state-of-the-art.

Consent is an important legal basis for the processing of personal data under the General Data Protection Regulation (GDPR), which is the current European data protection law. GPDR provides constraints and obligations on the validity of consent, and provides data subjects with the right to withdraw their consent at any time. Determining and demonstrating compliance to these obligations require information on how the consent was obtained, used, and changed over time. Existing work demonstrates feasibility of semantic web technologies in modelling information and determining compliance for GDPR. Although these address consent, they currently do not model all the information associated with it. In this paper, we address this by first presenting our analysis of information associated with consent under the GDPR. We then present GConsent, an OWL2-DL ontology for representation of consent and its associated information such as provenance. The paper presents the methodology used in the creation and validation of the ontology as well as an example use-case demonstrating its applicability. The ontology and this paper can be accessed online at https://w3id.org/GConsent .

Data.geohive.ie aims to provide an authoritative service for serving Ireland?s national geospatial data as Linked Data. The service currently provides information on Irish administrative boundaries and the boundaries used for the Irish 2011 census. The service is designed to support two use cases: serving boundary data of geographic features at various level of detail and capturing the evolution of administrative boundaries. In this paper, we report on the development of the service and elaborate on some of the informed decisions concerned with the URI strategy and use of named graphs for the support of aforementioned use cases ? relating those with similar initiatives. While clear insights on how the data is being used are still being gathered, we provide examples of how and where this geospatial Linked Data dataset is used.

Typically tools that map non-RDF data into RDF format rely on the technology native to the source of the data when manipulation of data during the mapping is required. Depending on the data format, data manipulation can be performed using underlying technology, such as RDBMS for relational databases or XPath for XML. For CSV/Tabular data there is no such underlying technology, and instead transforming the source data into another format or pre/post-processing techniques are used. As part of this paper, we present a comparison framework for the state-of-the-art in converting CSV/Tabular data into RDF, where a key feature evaluated is transformation functions. We argue that existing approaches for transformation functions in such tools are complex - in number of steps and tools involved - and therefore not as traceable and transparent as one would like. We tackle these problems by defining a more generic, usable and amenable method to incorporate functions into uplift mapping languages, called FunUL. As proof of concept, we show an implementation of our method. Moreover, by using a real world Digital Humanities case study, we compare our approach with other approaches that we have identified to include transformation functions as part of the mapping for CSV/Tabular data.

Ontology matching and mapping is concerned with discovering correspondences between two ontologies to create a mapping that enable applications to relate, interlink or integrate data. The construction of such mappings is not trivial as they are created to serve a purpose and result from collaboration between the different stakeholders. Current ontology-mapping metadata formats only capture a glimpse of the mapping construction process by focusing on the exchange of mappings and they provide some limited properties to facilitate reuse and discovery. For mapping governance to be possible, we argue that a suitable metadata model -- which will be presented in this paper -- needs to capture all aspects from the ontology mapping lifecycle: from the inception of a project to the execution of these mappings. This allows one to formulate queries that not only would facilitate the discovery and reuse, but also queries that allow one to govern the ontology mapping projects and render the construction processes more transparent and traceable.

Domain rules are important for businesses to obtain good data governance. Although efficient for storing and processing data, the use of popular semantic technologies alone does not suffice. As the Web is gaining a prominent role for enterprises (and communities in general), appropriate methods and tools are required for data governance, with a proper emphasis on facts in natural language. This paper presents Business Semantics Glossary that supports the a method called Business Semantics Management.