This work presents experiences and lessons learned in the development of ontologies using a combination of OWL for domain modeling and SHACL for the validation of RDF knowledge graphs that follow the OWL model. In our work, through a real-world use case in the domain of railway transport, we propose a joint OWL+SHACL development approach, including the use of SKOS for the representation of reference data, that is coupled to the definition of the ontology. In this approach, classes and properties are represented in both languages, and constraints may be represented in one or both languages according to the need for ontology reasoning and knowledge graph correctness requirements.

Reification in knowledge graphs has been present since the inception of RDF to allow capturing additional information in triples, usually metadata. The need of adopting or changing a metadata representation in a pre-existing graph to enhance the knowledge capture and access can lead to inducing complex structural changes in the graph, according the target representation’s schema. In these situations, it is necessary to decide whether to construct the knowledge graph again from its original sources, or to re-construct it using the current version of the graph. In this paper we conduct an empirical study to analyze which re-construction approach is more suitable for switching the representation approach from the created graph ensuring that the additional represented knowledge is preserved. We study four well-known metadata representations, using mapping languages to construct the graph, and SPARQL CONSTRUCT queries to re-construct it. With this work we aim to provide insights about the impact of re-construction on metadata representations interoperability and the implications of different approaches.

A large number of datasets are being made available on the Web using a variety of formats and according to diverse data models. Ontology Based Data Integration (OBDI) has been traditionally proposed as a mechanism to facilitate access to such heterogeneous datasets, providing a unified view over their data by means of ontologies. Recently, the term “Virtual Knowledge Graph Access” has begun to be used to refer to the mechanisms that provide query-based access to knowledge graphs virtually generated from heterogeneous data sources. Several OBDI engines exist in the state of the art, with overlapping capabilities but also clear differences among them (in terms of the data formats that they can deal with, mapping languages that they support, query expressivity that they allow, etc.). These engines have been evaluated with different testbeds and benchmarks. However, their heterogeneity has made it difficult to come up with a common comprehensive benchmark that allows for comparisons among them to facilitate their selection by practitioners, and more importantly, for their continuous improvement by the teams that maintain them. In this paper we present GTFS-Madrid-Bench, a benchmark to evaluate OBDI engines that can be used for the provision of access mechanisms to virtual knowledge graphs. Our proposal introduces several scenarios that aim at measuring the query capabilities, performance and scalability of all these engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They have been transformed into several formats (CSV, JSON, SQL and XML) and scaled up. The query set aims at addressing a representative number of SPARQL 1.1 features while covering usual queries that data consumers may be interested in.