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Born in the early 1980's as a multilingual agricultural thesaurus, AGROVOC has steadily evolved over the last fifteen years, moving to an electronic version around the year 2000, and embracing the Semantic Web shortly thereafter. Today AGROVOC is a SKOS-XL concept scheme published as Linked Open Data, containing links (as well as backlinks) and ref...
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... AGROVOC thesaurus -- its name is a portman- teau word of Agriculture with Vocabulary -- was first published at the beginning of the 1980s by the Food and Agriculture Organization of the United Nations (FAO). At its birth AGROVOC was available in three languages (English, Spanish and French), its purpose to serve as a controlled vocabulary for the indexing of publications in agricultural science and technology, including forestry, animal husbandry, aquatic sciences, fisheries, aquaculture and human nutrition. Primary users were the FAO library and the International Sys- tem for Agricultural Science and Technology (AGRIS) ( ), a global public domain database coordinated by FAO and containing approx- imately 3.5 million bibliographic records. In the year 2000, AGROVOC abandoned paper printing and went digital, with data storage handled by a relational database. This greatly eased maintenance. However, limitations were also experienced, espe- cially owing to the distributed community of editors which had enlarged over the years. Also, data were available to third parties only by means of database dumps, or through web services. The models and technologies developed within the Semantic Web, and the publication methodologies and best practices pro- moted by Linked Open Data [1] offered the possibility to overcome these limitations. AGROVOC was re- modelled using OWL [2] and then SKOS (see [3] for a detailed description of the evolution of the model). With the adoption of SKOS-XL, AGROVOC finally met the modelling requirements of a multilingual and linguistically detailed thesaurus. Today, the AGROVOC SKOS-XL concept scheme is a LOD (Linked Open Data) Dataset composed of more than 32000 concepts available in over 20 languages (five additional languages are under development), containing up to 40,000 terms in each language. AGROVOC is still managed by FAO, and owned and maintained by an international community of experts and institutions active in the area of agriculture. AGROVOC is widely used in specialized libraries as well as digital libraries and repositories to index content. It is also used as a specialized tagging resource for knowledge and content organization by FAO and other third-party stakeholders. This paper provides an overall description of the AGROVOC Linked Dataset and details its maintenance and publication process. As many thesaurus managers are embracing Semantic Web technologies, we believe our work is of general interest and may serve as a use case to the community. The rest of this paper is organized as follows: section two provides more details about publication of the linked dataset; section three presents the process followed for the generation of links between AGROVOC and relevant resources such as vocabularies, glossaries and thesauri; section four summarizes and discusses the entire data flow of AGROVOC, from maintenance to LOD publication; section five provides additional information on reported use of the AGROVOC linked Dataset and section six concludes. Information about AGROVOC thesaurus is available from the FAO website, at the address: The RDF version of AGROVOC has been made available as a Linked Open Dataset at the address: and it is also available through a data dump on the project site 1 of its main editing platform, VocBench (see [4] for a detailed description of this collaborative editing tool developed by FAO and other partners). AGROVOC data is freely usable under the terms of the Creative Commons 3.0 license 2 . Content negotiation for the LOD Dataset is properly managed by the server, and clients requesting HTML content (e.g. ordinary web browsers) are returned with an HTML representation of RDF data describing the requested concept, provided by the Pubby 3 application (see Figure 1). A description file following the VoID (Vocabulary of Interlinked Datasets) specifications [5] is available alongside the AGROVOC Linked Open Dataset: Such a VoID file contains statistical information about the linked dataset, as well as coordinates for automatically accessing and properly querying it. After the evolutions which its modelling exigencies dictated along the past years, AGROVOC finally found in the SKOS-XL model its perfectly-fitting dress. The SKOS-XL model, features “reified” labels which can thus be enriched with properties of their own. As an example, consider the concept shown in Figure 1, “maize” in English . Its Chinese preferred label 4 , “ 玉 米 ” , is expressed in skos-xl by means of two triples 5 ...
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... AGROVOC thesaurus -- its name is a portman- teau word of Agriculture with Vocabulary -- was first published at the beginning of the 1980s by the Food and Agriculture Organization of the United Nations (FAO). At its birth AGROVOC was available in three languages (English, Spanish and French), its purpose to serve as a controlled vocabulary for the indexing of publications in agricultural science and technology, including forestry, animal husbandry, aquatic sciences, fisheries, aquaculture and human nutrition. Primary users were the FAO library and the International Sys- tem for Agricultural Science and Technology (AGRIS) ( ), a global public domain database coordinated by FAO and containing approx- imately 3.5 million bibliographic records. In the year 2000, AGROVOC abandoned paper printing and went digital, with data storage handled by a relational database. This greatly eased maintenance. However, limitations were also experienced, espe- cially owing to the distributed community of editors which had enlarged over the years. Also, data were available to third parties only by means of database dumps, or through web services. The models and technologies developed within the Semantic Web, and the publication methodologies and best practices pro- moted by Linked Open Data [1] offered the possibility to overcome these limitations. AGROVOC was re- modelled using OWL [2] and then SKOS (see [3] for a detailed description of the evolution of the model). With the adoption of SKOS-XL, AGROVOC finally met the modelling requirements of a multilingual and linguistically detailed thesaurus. Today, the AGROVOC SKOS-XL concept scheme is a LOD (Linked Open Data) Dataset composed of more than 32000 concepts available in over 20 languages (five additional languages are under development), containing up to 40,000 terms in each language. AGROVOC is still managed by FAO, and owned and maintained by an international community of experts and institutions active in the area of agriculture. AGROVOC is widely used in specialized libraries as well as digital libraries and repositories to index content. It is also used as a specialized tagging resource for knowledge and content organization by FAO and other third-party stakeholders. This paper provides an overall description of the AGROVOC Linked Dataset and details its maintenance and publication process. As many thesaurus managers are embracing Semantic Web technologies, we believe our work is of general interest and may serve as a use case to the community. The rest of this paper is organized as follows: section two provides more details about publication of the linked dataset; section three presents the process followed for the generation of links between AGROVOC and relevant resources such as vocabularies, glossaries and thesauri; section four summarizes and discusses the entire data flow of AGROVOC, from maintenance to LOD publication; section five provides additional information on reported use of the AGROVOC linked Dataset and section six concludes. Information about AGROVOC thesaurus is available from the FAO website, at the address: The RDF version of AGROVOC has been made available as a Linked Open Dataset at the address: and it is also available through a data dump on the project site 1 of its main editing platform, VocBench (see [4] for a detailed description of this collaborative editing tool developed by FAO and other partners). AGROVOC data is freely usable under the terms of the Creative Commons 3.0 license 2 . Content negotiation for the LOD Dataset is properly managed by the server, and clients requesting HTML content (e.g. ordinary web browsers) are returned with an HTML representation of RDF data describing the requested concept, provided by the Pubby 3 application (see Figure 1). A description file following the VoID (Vocabulary of Interlinked Datasets) specifications [5] is available alongside the AGROVOC Linked Open Dataset: Such a VoID file contains statistical information about the linked dataset, as well as coordinates for automatically accessing and properly querying it. After the evolutions which its modelling exigencies dictated along the past years, AGROVOC finally found in the SKOS-XL model its perfectly-fitting dress. The SKOS-XL model, features “reified” labels which can thus be enriched with properties of their own. As an example, consider the concept shown in Figure 1, “maize” in English . Its Chinese preferred label 4 , “ 玉 米 ” , is expressed in skos-xl by means of two triples 5 ...
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... note that when providing a human reada- ble representation of the data ( Figure 1), we preferred to present our visitor the actual label, instead of the reified version of it. This is why the classical skos representation of terms is also available: Beyond enabling a finer linguistic modelling of the resource (by allowing, for instance, lexical relation- ships across labels without involvement of the at- tached concepts), SKOS-XL also makes it possible to refine the grain of editorial notes at language level by adding separate information on the revision of concepts as well as of each label in each language. Thus we are able to state that “ 玉 米 ” has a code, originating from the past pre-RDF versions of AGROVOC, equal to “12332” . This is expressed by the triple 6 : As a further example, we are also able to state that that term was created on December 12, 2002: The Agrontology seen in the above example is a compendium to AGROVOC, providing domain-spe- cific properties for enriching the description of concepts. Agrontology is enriched with VOAF 8 (Vocabulary of a Friend) descriptors, mostly for linking it to AGROVOC (and to other Datasets adopting it, such as the FAO Biotech Glossary 9 ), and to have it men- tioned in the LOV Dataset. Currently, AGROVOC is undergoing a deep analysis in order to make very explicit the modelling style adopted in its various topic areas (see Figure 2). A parallel analysis of the agron- tology is ongoing, with the purpose of reaching a full harmony between domain modelling and the vocabulary currently used for it. All 32,000+ concepts of the AGROVOC thesaurus are hierarchically organized under its 25 top concepts. AGROVOC top concepts are very general and high level concepts , including concepts such as “activities”, “organisms”, “locations”, “products”, “organism”, etc. The fact that 20,000+ concepts fall under the top concept “organism”, confirms how AGROVOC is largely oriented towards the agriculture sector (see Figure 2 for a complete statistic distribution of the dataset concepts under its 25 top concepts). Other important areas of AGROVOC include “substances”, “entities”, “products” and “locations”. Beyond being listed in the AGROVOC website, the list of top concepts can be found in the VoID file for AGROVOC Linked Open Dataset. Moreover, a deep study on the current coverage of AGROVOC is under study, with the purpose of supporting human and machine users alike in their quest for information within the thesaurus and its links. AGROVOC is today published as an Open Linked Dataset with links to thirteen vocabularies, thesauri and ontologies. Five of the linked resources are general in scope: the Library of Congress Subject Head- ings (LCSH) 10 , , RAMEAU Répertoire d'autorité- matière encyclopedique et alphabetique unifie, Eu- rovoc 12 , DBpedia 13 , and an experimental Linked Data version of the Dewey Decimal Classification 14 . The remaining eight resources are specific to various domains: NAL Thesaurus 15 for agriculture, GEMET 16 for environment, STW for Economics, TheSoz 17 for so- cial science and both GeoNames and the FAO Geopolitical Ontology cover countries and political regions. ASFA 18 covers aquatic science and the aptly named Biotechnology glossary covers biotechnology. These linked resources are mostly available as RDF/SKOS resources. The linked resources were considered in their entirety barring RAMEAU, for which only agriculture related concepts were considered (amounting to some 10% of its 150 000 concepts). Candidate mappings were found by applying string similarity matching al- gorithms to pairs of preferred labels [6] and by using the Ontology Alignment API [7] for managing the produced matches. The common analysis language used was English in all cases except the AGROVOC RAMEAU alignment for which French was used. Table 1 shows, for each resource linked to AGROVOC (column 1), its area of coverage (column 2), the language considered for mapping with AGROVOC (column 3), and the number of matches resulting from the evaluation (column 4, see below). Candidate links were presented to a domain expert for evaluation in the form of a spreadsheet. Once validated, the mappings were loaded in the same triple store where the linked data version of AGROVOC is stored. All resulting validated candidate matches were considered to be skos:exactMatch, as in agv:c_12332 skos:exactMatch ("maize" in English). Exact match has the advantage of a looser notion of equivalence than the more formal equivalence found in owl equivalence/identity properties such as owl:sameAs . The objective when linking AGROVOC to other resources was to provide only main anchors, privileging accuracy over recall. This is why we (mostly) rejected skos:closeMatch and relied exclusively on skos:exactMatch , found by means of string-similarity techniques as opposed to more sophisticated context-based approaches. Also, the One Sense per Domain assumption which, in analogy to the “one sense per discourse” [8] assumption, specifies that “t he more specialized a domain is, ...
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... Most consortia related to Life Science have turned to Bioschemas profiles as they already provide some guidance on how to use SchemaOrg in this domain. For instance, FAIRagro will build upon and extend Bioschemas specifications, taking also into account well-known vocabularies in the agri-domain (e.g., AgroVoc [7]). Work on extensions and adoption will involve a variety of domain experts, expert associations and service providers to work collaboratively, via two AgriHackathons. ...
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... However, such mappings can only be done between individuals of skos:Concept, and thus requires an additional step of converting aligned skos:Concept individuals into OWL classes to create a multilingual ontology. An example of SKOS-based mappings include the AGROVOC multilingual thesaurus, which uses cross-lingual mappings, where SKOS concepts are mapped to external vocabularies such as the Chinese Agricultural Thesaurus using skos:exactMatch and skos:closeMatch [54,55,56]. Annane et al. [57] used SKOS and the General Ontology for Linguistic Description (GOLD) [58] to generate 228 000 mappings between English ontologies on BioPortal and its French equivalent. ...
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... The LEAP4FNSSA lexicon is the combination between 3 semantic resources, i.e. inputs in order to construct the final lexicon: Agrovoc terms associated with these concepts are manually extracted from the online 1 resource. Agrovoc is a multilingual thesaurus dedicated to the agricultural domain devel-oped by FAO (Food and Agriculture Organization) [3] . This thesaurus is used for different applications, e.g. ...
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... This can be illustrated with FoodOn (Dooley et al., 2018), which was initially built to be used in collaboration with Genomic Epidemiology Ontology (GenEpiO 31 ) to specify foodborne disease risks and not food science or technology. AGROVOC, on the other hand, is a generic multilingual thesaurus developed by the Food and Agriculture Organisation (FAO) with direct interest for KT and covering many fields in agriculture and food (Caracciolo et al., 2013). ...
Background
Scientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.
Scope and approach
This paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.
Key findings and conclusions
The paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
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The presence of technologies in the agronomic field has the purpose of proposing the best solutions to the challenges found in agriculture, especially to the problems that affect cultivars. One of the obstacles found is to apply the use of your own language in applications that interact with the user in Brazilian Agribusiness. Therefore, this work uses Natural Language Processing techniques for the development of an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in soybean crop, stored in a non-relational database repository to provide accurate diagnostics to simplify the work of the farmer and the agricultural stakeholders who deal with a lot of information. In order to build dialogues and provide rich consultations, from agriculture manuals, a data structure with 108 pests and diseases with their information on the soybean cultivar and through the spaCy tool, it was possible to pre-process the texts, recognize the entities and support the requirements for the development of the conversacional system.
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With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
