TY - GEN
T1 - Coupling of semantic and syntactic graphs generated via tweets to detect local events
AU - Rajaonarivo, Landy
AU - Mine, Tsunenori
AU - Arakawa, Yutaka
N1 - Funding Information:
This work was supported by JSPS Kakenhi grant number JP21F21377 and NICT.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Local events are important for the local people, tourists and local authorities. However, information about them is limited, or even absent on official websites. We propose an approach to automatically generate a data graph on local events via social network and mobile positioning data. This graph is constructed by the research results of syntactic and semantic data collection and will be used for an intelligent local event recommendation application. The coupling of these two research results makes it possible to discover little-known events or places that are not in the Linked Open Data and at the same time to connect them to it. An online experiment was set up to evaluate the performance of the techniques used in the study on semantic data which concerns the automatic ontology generation. The results allow us to conclude that the performance of the Named Entity Recognition and the choice of threshold score in automatic ontology generation depend on the categories of detected words types.
AB - Local events are important for the local people, tourists and local authorities. However, information about them is limited, or even absent on official websites. We propose an approach to automatically generate a data graph on local events via social network and mobile positioning data. This graph is constructed by the research results of syntactic and semantic data collection and will be used for an intelligent local event recommendation application. The coupling of these two research results makes it possible to discover little-known events or places that are not in the Linked Open Data and at the same time to connect them to it. An online experiment was set up to evaluate the performance of the techniques used in the study on semantic data which concerns the automatic ontology generation. The results allow us to conclude that the performance of the Named Entity Recognition and the choice of threshold score in automatic ontology generation depend on the categories of detected words types.
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U2 - 10.1109/IIAIAAI55812.2022.00034
DO - 10.1109/IIAIAAI55812.2022.00034
M3 - Conference contribution
AN - SCOPUS:85139565804
T3 - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
SP - 128
EP - 133
BT - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
A2 - Matsuo, Tokuro
A2 - Takamatsu, Kunihiko
A2 - Ono, Yuichi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
Y2 - 2 July 2022 through 7 July 2022
ER -