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.
DiStRDF: Distributed Spatio-temporal RDF Queries on Spark Proceeding
FAIMUSS: Flexible Data Transformation to RDF from Multiple Streaming Sources Proceeding
The datAcron Ontology for Semantic Trajectories Conference
ESWC 2017, 2017.
Generating linked RDF data from heterogeneous streaming and archival data sources: Populating the datAcron ontology Conference
Semantics 2017, Amstrerdam, Holland, 2017.
Expert Systems With Applications, 2017.