TY - GEN
T1 - Towards 5G/6G Data Harmonization through NLP and Semantic Web Technologies
AU - Singh, Mandeep
AU - Mahmoud, Moatasim
AU - Stamatia, Rizou
AU - Zaharis, Zaharias D.
AU - Lazaridis, Pavlos I.
AU - Poulkov, Vladimir K.
AU - Wu, Wenyan
N1 - Funding Information:
This research was supported by the European Union through the Horizon 2020 Marie Sklodowska-Curie Research and Innovation Staff Exchange Programme \"Research Col-laboration and Mobility for Beyond 5G Future Wireless Networks (RECOMBINE)\" under Grant no. 872857. This work is financed by the European UnionNextGenerationEU through the National Recovery and Resilience Plan of the Re?public of Bulgaria, BG-RRP-2.005 -\"Twinning\" with Project No. BG-RRP-2.005-002 titled \"Twinning for Excellence in Research in Sustainable Future Communication Networks in the Context of a Green Economy GREENBEAT\".
Publisher Copyright:
© 2024 IEEE.
PY - 2025/3/19
Y1 - 2025/3/19
N2 - Telecommunication systems utilize several mechanisms to collect data from 5G/6G-enabled IoT. In the 5G/6G community, various AI techniques and tools are applied to 5G/6G data to monitor, predict, and make decisions. Therefore, 5G/6G data must be interoperable for monitoring, prediction, and decision support systems. However, 5G/6G data are typically mapped in local data models for local applications, which poses challenges to using them in different or cross-domain applications due to a lack of interoperability issues. In this paper, we propose an approach to support and enhance the interoperability of 5G/6G data through NLP and Semantic Web technologies to achieve 5G/6G data harmonization.
AB - Telecommunication systems utilize several mechanisms to collect data from 5G/6G-enabled IoT. In the 5G/6G community, various AI techniques and tools are applied to 5G/6G data to monitor, predict, and make decisions. Therefore, 5G/6G data must be interoperable for monitoring, prediction, and decision support systems. However, 5G/6G data are typically mapped in local data models for local applications, which poses challenges to using them in different or cross-domain applications due to a lack of interoperability issues. In this paper, we propose an approach to support and enhance the interoperability of 5G/6G data through NLP and Semantic Web technologies to achieve 5G/6G data harmonization.
KW - 5G/6G data
KW - data harmonization
KW - interoperability
KW - knowledge graph
KW - QoE
UR - http://www.scopus.com/inward/record.url?scp=105001918763&partnerID=8YFLogxK
U2 - 10.1109/ATOMS60779.2024.10921618
DO - 10.1109/ATOMS60779.2024.10921618
M3 - Conference contribution
AN - SCOPUS:105001918763
SN - 9798350358384
SP - 291
EP - 294
BT - 2024 IEEE Conference on Advanced Topics on Measurement and Simulation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Conference on Advanced Topics on Measurement and Simulation
Y2 - 28 August 2024 through 30 August 2024
ER -
