A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic

This paper addresses the problem of mapping equivalent items between two databases based on their textual descriptions. Specifically, we will apply this technique to link the elements of two food composition databases by calculating the most likely match of each item in another given database. A num...

Descripción completa

Detalles Bibliográficos
Autores principales: Morales-Garzón, Andrea, Gómez-Romero, Juan, Martin-Bautista, M. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274754/
http://dx.doi.org/10.1007/978-3-030-50143-3_50
_version_ 1783542653589127168
author Morales-Garzón, Andrea
Gómez-Romero, Juan
Martin-Bautista, M. J.
author_facet Morales-Garzón, Andrea
Gómez-Romero, Juan
Martin-Bautista, M. J.
author_sort Morales-Garzón, Andrea
collection PubMed
description This paper addresses the problem of mapping equivalent items between two databases based on their textual descriptions. Specifically, we will apply this technique to link the elements of two food composition 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.
format Online
Article
Text
id pubmed-7274754
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-72747542020-06-08 A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic Morales-Garzón, Andrea Gómez-Romero, Juan Martin-Bautista, M. J. Information Processing and Management of Uncertainty in Knowledge-Based Systems Article This paper addresses the problem of mapping equivalent items between two databases based on their textual descriptions. Specifically, we will apply this technique to link the elements of two food composition 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. 2020-05-15 /pmc/articles/PMC7274754/ http://dx.doi.org/10.1007/978-3-030-50143-3_50 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Morales-Garzón, Andrea
Gómez-Romero, Juan
Martin-Bautista, M. J.
A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title_full A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title_fullStr A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title_full_unstemmed A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title_short A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
title_sort word embedding model for mapping food composition databases using fuzzy logic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274754/
http://dx.doi.org/10.1007/978-3-030-50143-3_50
work_keys_str_mv AT moralesgarzonandrea awordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic
AT gomezromerojuan awordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic
AT martinbautistamj awordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic
AT moralesgarzonandrea wordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic
AT gomezromerojuan wordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic
AT martinbautistamj wordembeddingmodelformappingfoodcompositiondatabasesusingfuzzylogic

Ejemplares similares