• GDPR-compliant Data Sharing Employing Consent, Contracts, and Licenses. • Validating the CCV checks by utilizing SHACL which is more semantically compliant. • Introducing SHACL repairs to automatically fix data inconsistencies. • Securing the contract signing process by utilizing digital signatures. • Digital assets licensing through DALICC for the improvement of the CCV tool. GDPR defines six legal bases, at least one of which needs to be followed in order to process (or share) personally identifiable data in a lawful manner. Most of the research today is centered around the legal bases of consent and contracts. This limits the options for legal bases that one can select (or use) for data sharing, especially in circumstances where there is a need to use mul-tiple legal bases. For example, one can consent to share data but may want to place restrictions on how it can be used, which requires a license (an extension/add-on to data sharing contracts) in scenarios, where digital assets licensing is involved. Overcoming these limitations and en-abling data sharing via multiple legal bases require combining multiple legal bases. However, incorporating additional (or multiple) legal bases, such as licenses (as an add-on to contracts), in a GDPR-compliant manner remains a challenging task. This is because combining multiple legal bases requires an understanding of each individual legal basis—a task challenging in it-self—and designing a system in a manner that is both compliant with regulatory requirements and practically pertinent. Therefore, in this paper, we present our semantic-based approach and tool that enables GDPR-compliant data sharing via multiple legal bases, consent, and contracts (using licenses as an add-on). This work extends our previous work, GDPR Contract Com-pliance Verification (CCV) tool, which enables GDPR-compliant data sharing via consent and contracts only. We add licenses as a further add-on to contracts, make our previous work more semantically compliant by utilizing SHACL validation for compliance checking, secure the con-tract signing process with digital signatures, introduce SHACL repairs to automatically fix data inconsistencies, and evaluate the performance of the tool and the SHACL components. We demonstrate the effectiveness of SHACL and the enhancement of the tool with GDPR-complaint data sharing based on multiple legal bases by performance testing.

The Shapes Constraint Language (SHACL) is a recently standardized language for describing and validating constraints over RDF graphs. The SHACL specification describes the so-called validation reports, which are meant to explain to the users the outcome of validating an RDF graph against a collection of constraints. Specifically, explaining the reasons why the input graph does not satisfy the constraints is challenging. In fact, the current SHACL standard leaves it open on how such explanations can be provided to the users. In this paper, inspired by works on logic-based abduction and database repairs, we study the problem of explaining non-validation of SHACL constraints. In particular, in our framework non-validation is explained using the notion of a repair, i.e., a collection of additions and deletions whose application on an input graph results in a repaired graph that does satisfy the given SHACL constraints. We define a collection of decision problems for reasoning about explanations, possibly restricting to explanations that are minimal with respect to cardinality or set inclusion. We provide a detailed characterization of the computational complexity of those reasoning tasks, including the combined and the data complexity.

Knowledge-based recommender systems are well suited for users to explore complex knowledge domains like iconography without having domain knowledge. To help them understand and make decisions for navigation in the information space, we can show how important specific concept annotations are for the description of an item in a collection. We present an approach to automatically determine relevancy scores for concepts of a domain model. These scores represent the importance for item descriptions as part of knowledge-based recommender systems. In this paper we focus on the knowledge domain of iconography, which is quite complex, difficult to understand and not commonly known. The use case for a knowledge-based recommender system in this knowledge domain is the exploration of a museum collection of historical artworks. The relevancy scores for the concepts of an artwork should help the user to understand the iconographic interpretation and to navigate the collection based on personal interests.