Identification des unités de mesure dans les textes scientifiques

National audienceIdentification of units of measures in scientific texts. The work presented in this paper consists in identifying specialized terms (units of measures) in textual documents in order to enrich a onto-terminological resource (OTR). The first step permits to predict the localization of unit of measure variants in the documents. We have used a method based on supervised learning. This method permits to reduce significantly the variant search space staying in an optimal search context (reduction of 86% of the search space on the studied set of documents). The second step uses a new similarity measure identifying automatically variants associated with term denoting a unit of measure already present in the OTR with a precision rate of 82% for a threshold above 0.6 on the studied corpus.Le travail présenté dans cet article se situe dans le cadre de l'identification de termes spécialisés (unités de mesure) à partir de données textuelles pour enrichir une Ressource Termino-Ontologique (RTO). La première étape de notre méthode consiste à prédire la localisation des variants d'unités de mesure dans les documents. Nous avons utilisé une méthode reposant sur l'apprentissage supervisé. Cette méthode permet de réduire sensiblement l'espace de recherche des variants tout en restant dans un contexte optimal de recherche (réduction de 86% de l'espace de recherché sur le corpus étudié). La deuxième étape du processus, une fois l'espace de recherche réduit aux variants d'unités, utilise une nouvelle mesure de similarité permettant d'identifier automatiquement les variants découverts par rapport à un terme d'unité déjà référencé dans la RTO avec un taux de précision de 82% pour un seuil au dessus de 0.6 sur le corpus étudié

Units of measure identification in unstructured scientific documents in microbial risk in food

International audienceOBJECTIVE(S) A preliminary step in microbial risk assessment in food is to gather and capitalize experimental data. Data capitalization is a crucial stake in an overall decision support system which consists of predicting microbial behavior [1]. In the framework of the French ANR project MAP'OPT (Equilibrium Gas Composition in Modified Atmosphere Packaging and Food Quality), the predictive modeling platform Sym'Previus (www.symprevius.org) should be able to propose a global approach to establish a scientifically sound method for choosing an appropriate modified atmosphere and associated packaging solution. Our work is part of this overall system and aims at extracting semi-automatically experimental data from unstructured scientific documents. Indeed, these documents use natural language combined with domain-specific terminology that is extremely time-consuming and tedious to extract in the free form of text and therefore to gather and capitalize. Our work relies on the MAP'OPT-Onto ontology [4], which has been built as an extension of the ontology used in Sym'Previus by adding concepts about food packaging, quantity concepts and concepts managing units of measures. Experimental data are often expressed with concepts (e.g packaging, permeability) or a numerical value often followed with its unit of measure (e.g. 258 amol m-1 s-1 Pa-1). In this paper, our work deals with unit recognition, known as a scientific challenge. METHOD(S) Extracting automatically quantitative data is a painstaking process because units suffer from different ways of writing within documents. We can encounter same units written in different manners such as amol m-1 s-1 Pa-1 written as amol.m-1 .s-1 .Pa-1 or as amol/m/s/Pa. We aim at focusing on the extraction and identification of these variant units seen as synonyms, in order to enrich iteratively an ontology, which represents a predefined vocabulary used to annotate, capitalize and query experimental data extracted from texts [2]. Our work addresses unit extraction and identification issues from texts to enrich an ontology in a two-step approach. First, we use text-mining methods and supervised learning approaches in order to predict relevant parts of the text where synonyms of units or new units are. The second step of our method consists in extracting specific strings representing units in the segments of texts found in the previous step. The extracted candidates are compared to units already present in the ontology using a new edit measure based on Damerau-Levenshtein [3]. RESULTS We have made experiments on 115 scientific documents (i.e. around 35 000 sentences) on food packaging. Each unit is recognized from a list of 211 units already defined in the MAP'OPT-Onto. Our learning algorithms predict that almost 5 000 sentences contain units. This prediction is correct for 95,5% of cases. In the second step, we have successfully extracted 38 terms as either synonyms or new units from sentences selected in the first step. So, we can propose 18% of enrichment of the pre-existing MAP'OPT-Onto

Ontology Evolution for Experimental Data in Food

International audienceThroughout its life cycle, an ontology may change in order to adapt to domain changes or to new usages. This paper presents an ontology evolution activity [1] applied to an ontology dedicated to the annotation of experimental data in food [2], and a plug-in, DynarOnto, which assists ontology engineers for carrying out the ontology changes. Our evolution method is an a priori method which takes as input an ontology in a consistent state, implements the changes selected to be applied and manages all the consequences of those changes by producing an ontology in a consistent state

Extraction de relations n-aires interphrastiques guidée par une RTO

National audienceNous proposons dans cet article une méthode d'extraction d'instances de relations naires dans un texte guidée par une Ressource Termino-Ontologique (RTO) de domaine. Une RTO est une ressource comportant une composante conceptuelle (l'ontologie) et une composante terminologique (la terminologie), dans laquelle les termes sont distingués des concepts qu'ils dénotent. L'ontologie permet la modélisation de relations n-aires, reliant des arguments pouvant être des concepts symboliques et des quantités. La méthode proposée s'applique aux relations n-aires formulées de façon implicite dans le texte et dont les instances d'arguments peuvent être exprimées à travers différentes phrases du texte. ABSTRACT. We propose in this paper a method to extract instances of n-ary relations in a text guided by an Ontological and Terminological Resource (OTR). An OTR is a resource composed of a conceptual component (the ontology) and a terminological component (the terminology) in which the terms are distinguished from the concepts they denote. The ontology allows n-ary relationships to be described between arguments which can be symbolic concepts and quantities. The method is dedicated to the extraction of n-ary relations which are implicit in the text and whose instances of arguments may be expressed in different sentences of the text

Alignement d'ontologies : exploitation des ontologies liées sur le web de données

International audienceNous proposons dans cet article une méthode d’alignement d’une ontologie source avec des ontologies cibles déjà publiées et liées sur le web de données. Nous présentons ensuite un retour d’expérience sur l’alignement d’une ontologie dans le domaine des sciences du vivant et de l’environnement avec AGROVOC et NALT

A Decision Support Tool based on microbial safety prediction for a better dimensioning of modified atmosphere packaging

Predicting microbial safety of fresh products in Modified Atmosphere Packaging (MAP) systems implies to take into account the dynamic of O2 and CO2 exchanges in the system and its effect on microbial growth. In this purpose we coupled mathematical models of gas transfer (permeation through packaging and solubilisation / diffusion within food) with predictive microbiology models that take into account the effect of CO2 and O2 partial pressure in headspace and corresponding dissolved concentrations in the food. This mechanistic model was validated in simplified and in real conditions using dedicated challenge-tests performed on poultry meat, fresh salmon and processed cheese, inoculated with either Listeria monocytogenes or Pseudomonas fluorescens.Once validated, this model could be used as a Decision Support Tool in order to optimize the initial packaging atmosphere (level of O2 and CO2) and / or the geometry (ratio headspace volume to food mass). This tool could also be used to identify the packaging gas permeability the most suitable for maintaining the targeted % of gas initially flushed in the pack within a given tolerance. This approach permits a better dimensioning of MAP of fresh produce by selecting the packaging material fitted to “just necessary” (and not by default the most barrier one). The connexion of this model with dedicated databases gathering gas permeabilities of commonly used packaging materials allows us to obtain as output a ranking of the most suitable materials. This tool would be very useful for all stakeholders of the fresh produce chain. A demonstration of this Decision Support Tool is here proposed with the pipeline between mathematical models and related databases

A decision support system for eco-efficient biorefinery process comparison using a semantic approach

Enzymatic hydrolysis of the main components of lignocellulosic biomass is one of the promising methods to further upgrading it into biofuels. Biomass pre-treatment is an essential step in order to reduce cellulose crystallinity, increase surface and porosity and separate the major constituents of biomass. Scientific literature in this domain is increasing fast and could be a valuable source of data. As these abundant scientific data are mostly in textual format and heterogeneously structured, using them to compute biomass pre-treatment efficiency is not straightforward. This paper presents the implementation of a Decision Support System (DSS) based on an original pipeline coupling knowledge engineering (KE) based on semantic web technologies, soft computing techniques and environmental factor computation. The DSS allows using data found in the literature to assess environmental sustainability of biorefinery systems. The pipeline permits to: (1) structure and integrate relevant experimental data, (2) assess data source reliability, (3) compute and visualize green indicators taking into account data imprecision and source reliability. This pipeline has been made possible thanks to innovative researches in the coupling of ontologies, uncertainty management and propagation. In this first version, data acquisition is done by experts and facilitated by a termino-ontological resource. Data source reliability assessment is based on domain knowledge and done by experts. The operational prototype has been used by field experts on a realistic use case (rice straw). The obtained results have validated the usefulness of the system. Further work will address the question of a higher automation level for data acquisition and data source reliability assessment

Transition numérique et pratiques de recherche et d’enseignement supérieur en agronomie, environnement, alimentation et sciences vétérinaires à l’horizon 2040.

Pour citer ce document:Barzman M. (Coord.), Gerphagnon M. (Coord.), Mora O. (Coord.),Aubin-Houzelstein G., Bénard A., Martin C., Baron G.L, Bouchet F., Dibie-Barthélémy J., Gibrat J.F., Hodson S., Lhoste E., Moulier-Boutang Y., Perrot S., Phung F., Pichot C., Siné M., Venin T. 2019. Transition numérique et pratiques de recherche et d’enseignement supérieur en agronomie, environnement, alimentation et sciences vétérinaires à l’horizon 2040.INRA, France, 161pagesTransition numérique et pratiques de recherche et d’enseignement supérieur en agronomie, environnement, alimentation et sciences vétérinaires à l’horizon 2040

Experimental data annotation guided by an ontology for decision support: @Web project

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Using fuzzy conceptual graph rules to map ontologies

This paper presents a new ontology mapping method between a source ontology and a target one considered as a reference. Both ontologies are composed of triplets of the form (object, characteristic, value). Values describing the objects of the reference ontology are hierarchically organized using the a kind of relation. The proposed method considers the ontology mapping problem as a rule application problem in the Conceptual Graph model. First, a vocabulary common to both ontologies is defined using mapping between values and characteristics. Each value of the source ontology is associated with a fuzzy set of values of the reference ontology. Then, the source ontology is translated into a fuzzy conceptual graph base and the reference ontology into a conceptual graph rule base. Finally, rules are applied into the fact base in order to find correspondences between objects of both ontologies. This method is implemented and applied to the mapping of ontologies in risk assessment in food products, and experimental results are presented