A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic Morales Garzón, Andrea Gómez Romero, Juan Martín Bautista, María José Word embedding Fuzzy distance Database alignment 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. 2023-12-04T09:12:49Z 2023-12-04T09:12:49Z 2020 conference output 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 https://hdl.handle.net/10481/86008 https://doi.org/10.1007/978-3-030-50143-3_50 eng info:eu-repo/grantAgreement/EC/H2020/816303 http://creativecommons.org/licenses/by-nc-sa/4.0/ open access Atribución-NoComercial-CompartirIgual 4.0 Internacional Springer Nature