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52. Biocomposites from poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and lignocellulosic fillers: Processes stored in data warehouse structured by an ontology

Author

Munch, Mélanie, primary, Buche, Patrice, additional, Dervaux, Stéphane, additional, Breysse, Amélie, additional, Berthet, Marie-Alix, additional, David, Grégoire, additional, Lammi, Sarah, additional, Rol, Fleur, additional, Viretto, Amandine, additional, and Angellier-Coussy, Hélène, additional

Published
2022
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53. Towards Flexible Querying of XML Imprecise Data in a Dataware House Opened on the Web

Author

Buche, Patrice, Dibie-Barthélemy, Juliette, Haemmerlé, Ollivier, Houhou, Mounir, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Christiansen, Henning, editor, Hacid, Mohand-Saïd, editor, Andreasen, Troels, editor, and Larsen, Henrik Legind, editor

Published
2004
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57. Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables

Author

Lentschat, Martin, Buche, Patrice, Menut, Luc, Guari, Romane, Roche, Mathieu, Lentschat, Martin, Buche, Patrice, Menut, Luc, Guari, Romane, and Roche, Mathieu

Abstract

This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the ScienceDirect website. This dataset includes two sets of data: manual table annotation results and automatic data extraction results. The tables were first annotated by one annotator and cross-curated by three different annotators. The annotation task aimed to identify all table data dealing with packaging permeability measurements and compositions. An Ontological and Terminological Resource (OTR) was used for the annotation process. The annotation guidelines were drawn up through a collective iterative approach involving the annotators, and they may be accessed alongside the data. This dataset of n-Ary relations can be used in natural language processing (NLP) approaches implemented in experimental fields, especially for n-Ary relation extraction research. It can also be useful for training or evaluation of methods for the extraction of experimental data from tables and text in scientific documents, especially in experimental domains such as food packaging.

Published
2022

58. Results of the Ontology Alignment EvaluationInitiative 2022

Author

Abd Nikooie Pour, Mina, Algergawy, Alsayed, Buche, Patrice, Castro, Leyla J., Chen, Jiaoyan, Dong, Hang, Fallatah, Omaima, Faria, Daniel, Fundulaki, Irini, Hertling, Sven, He, Yuan, Horrocks, Ian, Huschka, Martin, Ibanescu, Liliana, Jimenez-Ruiz, Ernesto, Karam, Naouel, Laadhar, Amir, Lambrix, Patrick, Li, Huanyu, Li, Ying, Michel, Franck, Nasr, Engy, Paulheim, Heiko, Pesquita, Catia, Saveta, Tzanina, Shvaiko, Pavel, Trojahn, Cassia, Verhey, Chantelle, Wu, Mingfang, Yaman, Beyza, Zamazal, Ondrej, Zhou, Lu, Abd Nikooie Pour, Mina, Algergawy, Alsayed, Buche, Patrice, Castro, Leyla J., Chen, Jiaoyan, Dong, Hang, Fallatah, Omaima, Faria, Daniel, Fundulaki, Irini, Hertling, Sven, He, Yuan, Horrocks, Ian, Huschka, Martin, Ibanescu, Liliana, Jimenez-Ruiz, Ernesto, Karam, Naouel, Laadhar, Amir, Lambrix, Patrick, Li, Huanyu, Li, Ying, Michel, Franck, Nasr, Engy, Paulheim, Heiko, Pesquita, Catia, Saveta, Tzanina, Shvaiko, Pavel, Trojahn, Cassia, Verhey, Chantelle, Wu, Mingfang, Yaman, Beyza, Zamazal, Ondrej, and Zhou, Lu

Abstract

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by18 participants. This paper is an overall presentation of that campaign

Published
2022

59. Biocomposites from poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and lignocellulosic fillers: Processes stored in data warehouse structured by an ontology

Author

Munch, Mélanie, Buche, Patrice, Dervaux, Stéphane, Breysse, Amélie, Berthet, Marie-Alix, David, Grégoire, Lammi, Sarah, Rol, Fleur, Viretto, Amandine, Angellier-Coussy, Hélène, Munch, Mélanie, Buche, Patrice, Dervaux, Stéphane, Breysse, Amélie, Berthet, Marie-Alix, David, Grégoire, Lammi, Sarah, Rol, Fleur, Viretto, Amandine, and Angellier-Coussy, Hélène

Abstract

Due to the rising amount of plastic waste generated each year, multiple questions are emerging about their harmful long-term effects on the environment, the eco-systems and human health. One possible strategy to mitigate these issues is to substitute conventional plastics by materials fully biodegradable in natural conditions, such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). In order to decrease the overall cost and environmental impact of PHBV-based materials while modulating their technical performance, PHBV can be combined with lignocellulosic fillers. In this article, a total of 88 formulations of PHBV-based biocomposites has been collected, distributed over 5 interdisciplinary projects involving computer scientists, data scientists and biomass processing experts for food and bio-based material production. Available data concern the technical process descriptions, including the description of each step and the different observations measured.

Published
2022

66. Key Discovery for Numerical Data: Application to Oenological Practices

Author

Symeonidou, Danai, primary, Sanchez, Isabelle, additional, Croitoru, Madalina, additional, Neveu, Pascal, additional, Pernelle, Nathalie, additional, Saïs, Fatiha, additional, Roland-Vialaret, Aurelie, additional, Buche, Patrice, additional, Muljarto, Aunur-Rofiq, additional, and Schneider, Remi, additional

Published
2016
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67. Extraction et reconstitution de relation n-Aires issues d'articles scientifiques en domaine expérimental guideées par une Ressource Termino-Ontologique

Author

Lentschat, Martin, Buche, Patrice, Dibie, Juliette, and Roche, Mathieu

Abstract

We present different approaches to construct n-Ary relation instances from scientific articles in experimental domains (i.e. food packaging) driven by a Termino-Ontological Resource dealing with the food packaging domain. Our method starts with the identification of partial n-Ary relations in the document tables and seeks to complete them with argument instances from text of the articles. Our approach is based on a new multi-feature representation (i.e. STaRe - Scientific Table Representation) and the n-Ary relation reconstruction based on text-mining methods.

Published
2022

68. Alignement et enrichissement d’ontologies utilisées par deux systèmes d’informations gérant des données expérimentales sur les procédés de transformation : OpenSILEX-EnviBIS et PO2 BaGaTel

Author

Fernandez, Emilie, Weber, Magalie, Buche, Patrice, Cufi, Julien, Dervaux, Stéphane, Ibanescu, Liliana, Latrille, Eric, Neveu, Pascal, Rossard, Virginie, Tireau, Anne, and Weber, Magalie

Subjects
OpenSILEX-EnviBIS, données expérimentales, [INFO] Computer Science [cs], PO2 BaGaTel
Published
2022

69. Results of the ontology alignment evaluation initiative 2022

Author

Pour, Mina Abd Nikooie, Algergawy, Alsayed, Buche, Patrice, Castro, Leyla J., Chen, Jiaoyan, Dong, Hang, Fallatah, Omaima, Faria, Daniel, Fundulaki, Irini, Hertling, Sven, He, Yuan, Horrocks, Ian, Huschka, Martin, Ibanescu, Liliana, Jiménez-Ruiz, Ernesto, Karam, Naouel, Laadhar, Amir, Lambrix, Patrick, Li, Huanyu, Li, Yunhai, Michel, Franck, Nasr, Engy, Pesquita, Catia, Saveta, Tzanina, Paulheim, Heiko, Shvaiko, Pavel, Trojahn, Cassia, Verhey, Chantelle, Wu, Mingfang, Zamazal, Ondrej, Zhou, Lu, and Yaman, Beyza

Subjects
ontology
Abstract

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign.

Published
2022
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73. Towards decision support system for the agri-food sector using heterogeneous scientific data annotated with an ontology and Bayesian networks: a proof of concept applied to milk microfiltration

Author

Baudrit, Cédric, Buche, Patrice, Leconte, Nadine, Fernandez, Christophe, Belna, Maëllis, Gésan-Guiziou, Geneviève, Institut de Mécanique et d'Ingénierie (I2M), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM), CATI INRAE DIISCICO, Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Science et Technologie du Lait et de l'Oeuf (STLO), AGROCAMPUS OUEST, Société Boccard, Partenaires INRAE, Le séminaire est sponsorisé par le projet ANR D2KAB, and réseau 'Intégration de sources/masses de données hétérogènes et ontologies'

Subjects
predictive model, daztabase, computer calculation, food engineering, [SDV.IDA]Life Sciences [q-bio]/Food engineering, shim milk, microfiltration, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ontology
Abstract

International audience; With more than 9000 scientific articles publishedeach day, the scientific literature, includingexperimental data and knowledge, is a valuable sourceof information for developing predictivemodels to design decision support systems. However,scientific data are heterogeneously structured,found mainly in text format and expressed using differentvocabularies. This study developed apractical and versatile pipeline that combines ontology,databases and computer calculation toolsbased on the theory of belief functions and Bayesiannetworks. The objective was to design aniterative process that can structure a domain of knowledge,use domain ontology to integrate data instructured databases, and build a predictive modelfrom ontology and data. The ontology paradigm isused to help integrate data from heterogeneous sourcesand build the structure of the Bayesian network. The parameters of the Bayesian network are estimated using the integrated data taking intoaccount their reliability. The pipeline can be usediteratively to enrich the database and the modelwithnew data and knowledge over time without damagingthe entire system. The pipeline was assessedby applying it to a complex food engineering process:skimmed milk microfiltration. , which is one ofthe first operations that can be carried out afterharvesting. This application enabled buildingthedomain ontology of milk microfiltration to annotatestate-of-the-art literature sources, creatingastructured database and developing a predictivemodel of the permeation flux associated withoperating conditions.

Published
2021

74. How to Manage Incompleteness of Nutritional Food Sources?: A solution using FoodOn as pivot ontology

Author

Buche, Patrice, Cufi, Julien, Dervaux, Stéphane, Dibie, Juliette, Ibanescu, Liliana, Oudot, Alrick, Weber, Magalie, Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and FUI Metyl@b Project financed by BPI France

Subjects
Ontology Alignment, Ontology Building, Incompleteness Management, Background Knowledge, LanguaL, [INFO]Computer Science [cs], FoodOn, ANSES, USDA, Food Composition Databases
Abstract

International audience; In order to correctly assess the nutritional quality of a raw or manufactured food product, the first step is to obtain the associated nutritional values. Food composition databases (FCDBs) managed at national level provide values for nutrients of foods. Unfortunately, values associated with some nutrients of interest may be lacking in the FCDB of the country in which the nutritional quality must be assessed and finding values associated with nutrients for similar foods in other FCDBs is a way to deal with incompleteness. An additional issue arises because the vocabulary used to denote a given food in a given FCDB is usually different from the one used in others. In this paper, the authors address the problem of retrieving the nutritional value of foods by querying different FCDBs through FoodOn used as pivot ontology. The article presents a new food source alignment method between two FCDBs. The method has been evaluated on the French and United States food nutritional evaluation. The proposed solution for the incompleteness management task has been assessed with a real use case.

Published
2021

81. Combinaison de mesures lexicales et sémantiques pour l'extraction de données expérimentales dans des articlesscientifiques

Author

Lentschat, Martin, Buche, Patrice, Dibie-Barthelemy, Juliette, Roche, Mathieu, Université de Montpellier (UM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2021

82. Combinaison de mesures lexicales et sémantiques pour l'extraction de données expérimentales dans des articles scientifiques

Author

Lentschat, Martin, Buche, Patrice, Dibie-Barthélemy, Juliette, Roche, Mathieu, and Dibie, Juliette

Subjects
U10 - Informatique, mathématiques et statistiques, Analyse de données, [INFO] Computer Science [cs], Fouille de textes, Extraction d'information, fouille de données, Perméabilité des emballages, C30 - Documentation et information, Q80 - Conditionnement, Conditionnement des aliments, ComputingMilieux_MISCELLANEOUS, Traitement des données
Abstract

Cet article présente une méthode pour représenter et mesurer la pertinence de données expérimentales extraites d’articles scientifiques. Dans le domaine étudié, les emballages alimentaires, le nombre de documents est réduit et ceux-ci contiennent un vocabulaire spécifique. Nous utilisons une Ressource Termino-ontologique (RTO) pour guider l’extraction, les approches par apprentissage n’étant pas adaptées à la taille du corpus. La RTO définit les entités d’intérêt et les décrits à travers un vocabulaire. Les informations recherchées sont liées aux relations de perméabilité et sont de deux types : symboliques (i.e. une expression lexicale) et quantitatives (i.e. une valeur numérique et son unité de mesure).Les documents contiennent un grand nombre de faux-positifs dû à la présence d’informations n’étant pas liées à la perméabilité des emballages (par exemple, un nom d’emballage cité à titre de comparaison ou une température autre que le paramètre de contrôle de la mesure de perméabilité). Dans ce contexte, nous proposons ici une méthode complète et originale qui intègre une représentation multi-descripteurs des entités extraites permettant de calculer et combiner des scores de pertinence.

Published
2021

83. Food packaging permeability and composition dataset dedicated to text-mining

Author

Lentschat, Martin, Buche, Patrice, Dibie-Barthélemy, Juliette, Menut, Luc, Roche, Mathieu, Lentschat, Martin, Buche, Patrice, Dibie-Barthélemy, Juliette, Menut, Luc, and Roche, Mathieu

Abstract

This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

Published
2021

84. Towards Large-Size Ontology Alignment by Mapping Groups of Concepts

Author

Buche, Patrice, Dibie-Barthélemy, Juliette, Ibănescu, Liliana, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Meersman, Robert, editor, Dillon, Tharam, editor, and Herrero, Pilar, editor

Published
2010
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86. A new alignment method based on FoodOn as pivot ontology to integrate nutritional legacy data sources

Author

Buche, Patrice, Cufi, Julien, Dervaux, Stéphane, Dibie, Juliette, Ibanescu, Liliana, Oudot, Alrick, Weber, Magalie, Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ibanescu, Liliana, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), and AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Ontology alignment, LanguaL, [INFO]Computer Science [cs], Food composition databases, [INFO] Computer Science [cs], FoodOn
Abstract

International audience; In order to correctly assess the nutritional quality of a meal or a manufactured food product in a given country, the first step is to assess the nutritional values for its ingredients. Food composition databases (FCDBs) available in a lot of countries and managed at national level provide values for energy and nutrients of food components. Unfortunately, values associated with some nutrients of interest may be lacking in the FCDB of the country in which the nutritional quality must be assessed. Finding values associated with nutrients for similar foods in other FCDBs is a way to deal with incompleteness. An additional issue arises because the vocabulary used to describe the ingredients of a meal or a recipe in a given FCDB is usually different from the one used in other ones. In this paper we address the problem of identifying the nutritional value of recipe's ingredients by querying different FCDBs through FoodOn as pivot ontology. We present a new alignment method between two distinct FCDBs, based on syntactic and semantic approaches, whose vocabulary is previously transformed into an ontology. Our method has been evaluated on Ciqual, the French food nutritional database, USDA, the United States food nutritional database and the FoodOn ontology. The incompleteness management task based on FoodOn as pivot ontology has been assessed with a real use-case concerning iron, Vitamin B12, Vitamin C nutrients.

Published
2020

87. Food transformation process description using PO2 and FoodOn

Author

Buche, Patrice, Cufi, Julien, Dervaux, Stéphane, Dibie, Juliette, Ibanescu, Liliana, Oudot, Alrick, Weber, Magalie, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Graphs for Inferences on Knowledge (GRAPHIK), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ibanescu, Liliana, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], ontology ecosystem, observation, semantic integration, food transformation process, domain ontology, food component modeling, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; The food production and processing sector are facing sustainability challenges of growing complexity. To tackle these challenges, data and knowledge from many different domains may be structured and stored using an ontol-ogy, a semantic model. In this paper we present a core ontology designed to model processes and observations from food domains. Three datasets struc-tured according with this ontology are stored into a repository. Dedicated tools were designed to assist domain experts in integrating and querying data. Semantic integration of data from food transformation domains may enable new decision support tools for new products with good qualities and eco-friendly properties.

Published
2020

90. Argumentation corrected context weighting-life cycle assessment: A practical method of including stakeholder perspectives in multi-criteria decision support for LCA

Author

Sohn, Joshua, Bisquert, Pierre, Buche, Patrice, Hecham, Abdelraouf, Kalbar, Pradip P., Goldstein, Ben, Birkved, Morten, Olsen, Stig Irving, Sohn, Joshua, Bisquert, Pierre, Buche, Patrice, Hecham, Abdelraouf, Kalbar, Pradip P., Goldstein, Ben, Birkved, Morten, and Olsen, Stig Irving

Abstract

Despite advances in the data, models, and methods underpinning environmental life cycle assessment (LCA), it remains challenging for practitioners to effectively communicate and interpret results. These shortcomings can bias decisions and hinder public acceptance for planning supported by LCA. This paper introduces a method for interpreting LCA results, the Argumentation Corrected Context Weighting-LCA (ArgCW-LCA), to overcome these barriers. ArgCW-LCA incorporates stakeholder preferences, corrects unjustified disagreements, and allows for the inclusion of non-environmental impacts (e.g., economic, social, etc.) using a novel weighting scheme and the application of multi-criteria decision analysis to provide transparent and context-relevant decision support. We illustrate the utility of the method through two case studies: a hypothetical decision regarding energy production and a real-world decision regarding polyphenol extraction technologies. In each case, we surveyed a relevant stakeholder group on their environmental views and fed their responses into the model to provide decision support that is relevant to their perspective. We found marked differences between results using ArgCW-LCA and results from a conventional analysis using an equal-weighting scheme, as well as differentiation between stakeholder preference groups, indicating the importance of applying the perspective of the particular stakeholder group. For instance, there was a rank reversal of alternatives when comparing between an equal weighting approach for all environmental and economic dimensions and ArgCW-LCA. ArgCW-LCA provides opportunity for both public and private sector incorporation of LCA, such as in developing enlightened stakeholder value measures. This is achieved through enabling the LCA practition to provide public and private actors' interpreted LCA results in a manner that incorporates educated stakeholder perspectives. Furthermore, the method encourages stakeholder multiplicity th

Published
2020

91. Fuzzy sets defined on a hierarchical domain

Author

Thomopoulos, Rallou, Buche, Patrice, and Haemmerle, Ollivier

Subjects
Domain structure -- Analysis, Fuzzy algorithms -- Usage, Fuzzy logic -- Usage, Fuzzy systems -- Usage, Knowledge acquisition (Expert systems) -- Research, Fuzzy logic, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper presents a new type of fuzzy sets, called 'Hierarchical Fuzzy Sets,' that apply when the considered domain of values is not 'flat,' but contains values that are more specific than others according to the 'kind of' relation. We study the properties of such fuzzy sets, that can be defined in a short way on a part of the hierarchy, or exhaustively (by their 'closure') on the whole hierarchy. We show that hierarchical fuzzy sets form equivalence classes in regard to their closures and that each class has a particular representative called 'minimal fuzzy set.' We propose a use of this minimal fuzzy set for query enlargement purposes and, thus, present a methodology for hierarchical fuzzy set generalization. We finally present an experimental evaluation of the theoretical results described in the paper, in a practical application. Index Terms--Fuzzy set, uncertainty, 'fuzzy' and probabilistic reasoning, object hierarchies, relaxation, knowledge retrieval.

Published
2006

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