TY - GEN
T1 - Automatic Generation of Event Ontology from Social Network and Mobile Positioning Data
AU - Rajaonarivo, Landy
AU - Mine, Tsunenori
AU - Arakawa, Yutaka
N1 - Funding Information:
This work was partially supported by Japan Society for the Promotion of Science, and the National Institute of Information and Communications Technology (NICT).
Publisher Copyright:
© 2021 ACM.
PY - 2021/12/14
Y1 - 2021/12/14
N2 - The study of mobile positioning data makes it possible to detect whether an event has happened at a particular place during a given period. However, determining the nature and details of the event is a challenge, especially if the event is not widely known, as is the case for local events. We propose an approach to determining the nature of local events by generating an ontology in a completely automatic way from social network data and data on people's movements and by querying this generated ontology. This approach uses entity discovery techniques, filtering systems and information enrichment via Open Data, as well as a system for matching discovered entities and ontology elements. Evaluation via a survey allowed us to validate approximately that the information presented in the ontology is reliable, makes sense and answers our questions.
AB - The study of mobile positioning data makes it possible to detect whether an event has happened at a particular place during a given period. However, determining the nature and details of the event is a challenge, especially if the event is not widely known, as is the case for local events. We propose an approach to determining the nature of local events by generating an ontology in a completely automatic way from social network data and data on people's movements and by querying this generated ontology. This approach uses entity discovery techniques, filtering systems and information enrichment via Open Data, as well as a system for matching discovered entities and ontology elements. Evaluation via a survey allowed us to validate approximately that the information presented in the ontology is reliable, makes sense and answers our questions.
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U2 - 10.1145/3486622.3493933
DO - 10.1145/3486622.3493933
M3 - Conference contribution
AN - SCOPUS:85128664800
T3 - ACM International Conference Proceeding Series
SP - 87
EP - 94
BT - Proceedings - 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
PB - Association for Computing Machinery
T2 - 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
Y2 - 14 December 2021 through 17 December 2021
ER -