LAUD: sociaL mediA fUsion moDule
Welcome to LAUD
This module combines the augmented knowledge schemas produced in the DIAPASON and LAUD modules, where the quality of aggregated data is enhanced by rejecting irrelevant information, minimizing redundancy, resolving inconsistencies, and completing missing information. This fusion process makes use of the ALLEGRO’s knowledge base containing domain ontologies to complete the knowledge schemas provided by the Data Analysis modules and to create other knowledge-based models to represent the description of the problem or situation of interest that has been inferred. Finally, from the ALLEGRO data layer, this description is forwarded to the application layer of the system to be consumed by third-party applications.
Research Team
Andrés Muñoz Ortega
(Module Coordinator)
Universidad de Cádiz
Andrés Bueno Crespo
Universidad Católica de Murcia
Francisco Arcas Túnez
Universidad Católica San Antonio de Murcia
Fernando Terroso Sáenz
Universidad Católica de Murcia
Antonia Sánchez Pérez
Universidad Católica de Murcia
Manuel Curado Navarro
Universidad Católica de Murcia
Magdalena Cantabella Sabater
Universidad Católica de Murcia
Belén López Ayuso
Universidad Católica de Murcia
Joaquín Lasheras Velasco
Centro Tecnológico de las Tecnologías de la Información y las Comunicaciones (CENTIC), Murcia
Acknowledgement
LAUD is part of the research project "Smart multi-modal crowdsensing-based system as a service oriented to the prediction of social problems (ALLEGRO)", grant PID2020-112827GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.