Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.

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/

RDF graphs are often generated by mapping data in other (semi-)structured data formats to RDF. Such mapped graphs have a repetitive structure defined by (i) the mapping rules and (ii) the schema of the input sources. However, this information is not exploited beyond its original scope. SHACL was recently introduced to model constraints that RDF graphs should validate. SHACL shapes and their constraints are either manually defined or derived from ontologies or RDF graphs. We investigate a method to derive the shapes and their constraints from mapping rules, allowing the generation of the RDF graph and the corresponding shapes in one step. In this paper, we present RML2SHACL: an approach to generate SHACL shapes that validate RDF graphs defined by RML mapping rules. RML2SHACL relies on our proposed set of correspondences between RML and SHACL constructs. RML2SHACL covers a large variety of RML constructs, as proven by generating shapes for the RML test cases. A comparative analysis shows that shapes generated by RML2SHACL are similar to shapes generated by ontology-based tools, with a larger focus on data value-based constraints instead of schema-based constraints. We also found that RML2SHACL has a faster execution time than data-graph based approaches for data sizes of 90MB and higher.