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.
Specific Topics
- Distributed reasoning with modular ontologies
- Ontology modularization and data partitioning for scalable information management and reasoning
- Ontology alignment methods
- Ontology engineering and management
Recent Publications
2018 |
DiStRDF: Distributed Spatio-temporal RDF Queries on Spark Proceeding 2018. |
FAIMUSS: Flexible Data Transformation to RDF from Multiple Streaming Sources Proceeding 2018. |
2017 |
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. |
OBDAIR: Ontology-Based Distributed Framework for Accessing, Integrating and Reasoning with Data in Disparate Data Sources Journal Article Expert Systems With Applications, 2017. |