A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
Identificadores
URI: https://hdl.handle.net/10481/86008Metadatos
Mostrar el registro completo del ítemEditorial
Springer Nature
Materia
Word embedding Fuzzy distance Database alignment
Fecha
2020Referencia bibliográfica
Morales-Garzón, A., Gómez-Romero, J., Martin-Bautista, M.J. (2020). A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1238. Springer, Cham. https://doi.org/10.1007/978-3-030-50143-3_50
Patrocinador
European Union under grant agreement No. 816303 (Stance4Health)Resumen
This paper addresses the problem of mapping equivalent
items between two databases based on their textual descriptions. Specif-
ically, we will apply this technique to link the elements of two food com-
position databases by calculating the most likely match of each item
in another given database. A number of experiments have been carried
by employing different distance metrics, some of them involving Fuzzy
Logic. The experiments show that the mappings are highly accurate and
Fuzzy Logic improves the precision of the model.