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.
Big Data Analytics for Time Critical Mobility Forecasting: From raw data to trajectory-oriented mobility analytics in the aviation and maritime domains Book Forthcoming
Parallel and scalable processing of spatio-temporal RDF queries using spark Journal Article Forthcoming
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.
Journal Of Data Semantics, 8 , 2019.