Text mining tools for extracting information about microbial biodiversity in food.
Algorithms
Biodiversity
Biological Ontologies
Computational Biology
/ methods
Data Mining
/ methods
Databases, Bibliographic
Databases, Factual
Ecosystem
Food Microbiology
Humans
Information Services
Information Storage and Retrieval
Internet
Literature
MEDLINE
National Library of Medicine (U.S.)
Phenotype
Phylogeny
PubMed
Software
United States
Food spoilage
Information extraction
Microbial biodiversity
Text mining
Journal
Food microbiology
ISSN: 1095-9998
Titre abrégé: Food Microbiol
Pays: England
ID NLM: 8601127
Informations de publication
Date de publication:
Aug 2019
Aug 2019
Historique:
received:
08
11
2017
revised:
26
03
2018
accepted:
17
04
2018
entrez:
27
3
2019
pubmed:
27
3
2019
medline:
6
5
2019
Statut:
ppublish
Résumé
Information on food microbial diversity is scattered across millions of scientific papers. Researchers need tools to assist their bibliographic search in such large collections. Text mining and knowledge engineering methods are useful to automatically and efficiently find relevant information in Life Science. This work describes how the Alvis text mining platform has been applied to a large collection of PubMed abstracts of scientific papers in the food microbiology domain. The information targeted by our work is microorganisms, their habitats and phenotypes. Two knowledge resources, the NCBI taxonomy and the OntoBiotope ontology were used to detect this information in texts. The result of the text mining process was indexed and is presented through the AlvisIR Food on-line semantic search engine. In this paper, we also show through two illustrative examples the great potential of this new tool to assist in studies on ecological diversity and the origin of microbial presence in food.
Identifiants
pubmed: 30910089
pii: S0740-0020(17)31063-8
doi: 10.1016/j.fm.2018.04.011
pmc: PMC6460834
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
63-75Informations de copyright
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
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