Combining ontologies by discovering semantic associations at any level of abstraction, or decomposing large ontologies in modules w.r.t. logical properties either for re-usability of specifications, efficiency in ontology management, or efficiecy in reasoning, are goals that we need to pursue, especially when we deal with real-world heterogeneous and disparate data sources.
These tasks are especially important towards data-driven ontology engineering methods.
Scalable enrichment of mobility data with weather information Journal Article
GEOINFORMATICA, 25 , pp. 291-309, 2021.
Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations Journal Article
The Knowledge Engineering Review, 35 , 2020.
The delta big data architecture for mobility analytics Conference
2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE IEEE, Oxford, UK, 2020.
The datAcron Ontology for the Specification of Semantic Trajectories: Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics Journal Article
Journal Of Data Semantics, 8 , 2019.
Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases Conference
SAC 2019, 2019.