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
2021 |
Nikolaos Koutroumanis Georgios M. Santipantakis, Apostolos Glenis Christos Doulkeridis & George Vouros A Scalable enrichment of mobility data with weather information Journal Article GEOINFORMATICA, 25 , pp. 291-309, 2021. @article{Koutroumanis2021, title = {Scalable enrichment of mobility data with weather information}, author = {Nikolaos Koutroumanis, Georgios M. Santipantakis, Apostolos Glenis, Christos Doulkeridis & George A. Vouros }, doi = {10.1007/s10707-020-00423-w}, year = {2021}, date = {2021-09-17}, journal = {GEOINFORMATICA}, volume = {25}, pages = {291-309}, abstract = {More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate trajectory prediction, as well as in air-traffic management for predicting regulations and reducing delays. Motivated by such applications, in this paper, we present a system for the enrichment of mobility data with weather information. Our main application scenario concerns streaming positional information (such as GPS traces of vehicles) that is collected and is enriched in an online fashion with stored weather data. We present the system architecture of a centralized version that runs on a single machine and exploits caching to improve its efficiency. Also, we extend our approach to a parallel implementation on top of Apache Kafka, which can scale to hundreds of thousands of processed records when provided with more computing nodes. Furthermore, we present extensions of our system for: (a) enrichment of more complex geometries than point data, and (b) providing linked RDF data as output. Our experimental evaluation on a medium-sized cluster shows the scalability of our approach in terms of number of processed records per second.}, keywords = {}, pubstate = {published}, tppubtype = {article} } More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate trajectory prediction, as well as in air-traffic management for predicting regulations and reducing delays. Motivated by such applications, in this paper, we present a system for the enrichment of mobility data with weather information. Our main application scenario concerns streaming positional information (such as GPS traces of vehicles) that is collected and is enriched in an online fashion with stored weather data. We present the system architecture of a centralized version that runs on a single machine and exploits caching to improve its efficiency. Also, we extend our approach to a parallel implementation on top of Apache Kafka, which can scale to hundreds of thousands of processed records when provided with more computing nodes. Furthermore, we present extensions of our system for: (a) enrichment of more complex geometries than point data, and (b) providing linked RDF data as output. Our experimental evaluation on a medium-sized cluster shows the scalability of our approach in terms of number of processed records per second. |
2020 |
Kotis, Konstantinos; Vouros, George A; Spiliotopoulos, Dimitris Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations Journal Article The Knowledge Engineering Review, 35 , 2020. @article{358, title = {Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations}, author = {Konstantinos Kotis and George A Vouros and Dimitris Spiliotopoulos}, url = {https://doi.org/10.1017/S0269888920000065}, doi = {https://doi.org/10.1017/S0269888920000065}, year = {2020}, date = {2020-01-01}, journal = {The Knowledge Engineering Review}, volume = {35}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, George A; Glenis, Apostolis; Doulkeridis, Christos 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. @conference{359, title = {The delta big data architecture for mobility analytics}, author = {George A Vouros and Apostolis Glenis and Christos Doulkeridis}, year = {2020}, date = {2020-01-01}, booktitle = {2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)}, publisher = {IEEE}, address = {Oxford, UK}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
2019 |
G.Vouros, ; Santipantakis, G; Doulkeridis, C; Vlachou, A; Andrienko, G; Andrienko, N; Fuchs, G; Martinez, Miguel Garcia; Cordero, Jose Manuel Garcia Journal Of Data Semantics, 8 , 2019. @article{352, title = {The datAcron Ontology for the Specification of Semantic Trajectories: Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics}, author = {G.Vouros and G Santipantakis and C Doulkeridis and A Vlachou and G Andrienko and N Andrienko and G Fuchs and Miguel Garcia Martinez and Jose Manuel Garcia Cordero}, url = {http://link.springer.com/article/10.1007/s13740-019-00108-0}, doi = {10.1007/s13740-019-00108-0}, year = {2019}, date = {2019-11-01}, journal = {Journal Of Data Semantics}, volume = {8}, chapter = {235}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, G; Vlachou, A; Doulkeridis, C; Glenis, A; Santipantakis, G Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases Conference SAC 2019, 2019. @conference{346, title = {Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases}, author = {G Vouros and A Vlachou and C Doulkeridis and A Glenis and G Santipantakis}, year = {2019}, date = {2019-01-01}, booktitle = {SAC 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |