Julián Arenas-Guerrero

Universidad Politécnica de Madrid | UPM · Departamento de Inteligencia Artificial

Knowledge graphs are often constructed from heterogeneous data sources, using declarative rules that map them to a target ontology and materializing them into RDF. When these data sources are large, the materialization of the entire knowledge graph may be computationally expensive and not suitable for those cases where a rapid materialization is re...

The ICT infrastructures of medium and large organisations that offer ICT services (infrastructure, platforms, software, applications, etc.) are becoming increasingly complex. Nowadays, these environments combine all sorts of hardware (e.g., CPUs, GPUs, storage elements, network equipment) and software (e.g., virtual machines, servers, microservices...

RDF-star was recently proposed as a convenient representation to annotate statements in RDF with metadata by introducing the so-called RDF-star triples, bridging the gap between RDF and property graphs. However, even though there are many solutions to generate RDF graphs, there is no systematic approach so far to generate RDF-star graphs from heter...

Knowledge graphs have proven to be a powerful technology to integrate and structure the myriad of data available nowadays. The semantic web community has actively worked on data integration systems, providing an important set of engines and mapping languages to facilitate the construction of knowledge graphs. Despite these important efforts, there...

In the current context of data prevalence, knowledge graphs have emerged as a popular method to integrate and extract value of heterogeneous large-scale data sources. In this thesis, we propose a novel idea for efficiently computing queries in a knowledge graph: the optimization of its physical design by combining materialization and virtualization...