knowledge organization system
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2021 ◽  
Vol 9 (1) ◽  
pp. 17
Author(s):  
Esther Mietzsch ◽  
Daniel Martini ◽  
Kristin Kolshus ◽  
Andrea Turbati ◽  
Imma Subirats

AGROVOC is the multilingual thesaurus managed and published by the Food and Agriculture Organization of the United Nations (FAO). Its content is available in more than 40 languages and covers all the FAO’s areas of interest. The structural basis is a resource description framework (RDF) and simple knowledge organization system (SKOS). More than 39,000 concepts identified by a uniform resource identifier (URI) and 800,000 terms are related through a hierarchical system and aligned to knowledge organization systems. This paper aims to illustrate the recent developments in the context of AGROVOC and to present use cases where it has contributed to enhancing the interoperability of data shared by different information systems.


Author(s):  
María-Luisa Alvite-Díez ◽  
M. Mercedes Martínez-González

Los tesauros se han adaptado progresivamente a los entornos digitales demostrando su capacidad de integración con las tecnologías de la Web Semántica. No obstante, la convergencia de tesauros y Web Semántica no es tan directa como aparentemente pudiera parecer. Este trabajo analiza y contrasta los constructos y reglas de integridad de ISO 25964 y SKOS (Simple Knowledge Organization System). Se examinan igualmente las representaciones en SKOS de los tesauros AGROVOC, EuroVoc y Unesco con el fin de estudiar las soluciones llevadas a cabo. Por último, se abordan los retos percibidos, considerando particularmente la integración de los tesauros con los datos enlazados y el desarrollo de ontologías.


Linha D Água ◽  
2021 ◽  
Vol 34 (2) ◽  
pp. 26-46
Author(s):  
Bruno Almeida

Este artigo parte do pressuposto de que as ferramentas utilizadas para a organização do conhecimento (tesauros, esquemas de classificação, etc.) podem ser entendidas como recursos terminológicos. Abordamos as relações entre terminologia e organização do conhecimento, assumindo um ponto de vista baseado na bidimensionalidade (linguística e conceptual) da terminologia enquanto disciplina. Em seguida, propomos uma análise dos conceitos subjacentes às designações “linguagem documental”, “vocabulário controlado” e “sistema de organização do conhecimento” nos textos de especialidade. Terminamos com a descrição do SKOS (Simple Knowledge Organization System), um modelo para a representação de sistemas de organização do conhecimento na web semântica, o qual é avaliado em termos da sua capacidade de modelizar recursos terminológicos de acordo com a abordagem bidimensional à terminologia e os principais elementos da norma ISO 1087.


Author(s):  
Elie Saliba ◽  
Régine Vignes Lebbe ◽  
Alain Dubois ◽  
Annemarie Ohler

Zoological nomenclature, the discipline of taxonomy responsible for managing the scientific names of animal taxa, takes its roots in the work of Carolus Linnaeus, and has been governed by an international Code since the beginning of the 20th century. Like any other scientific discipline, it has developed its own vocabulary, which has gotten increasingly complex with time. However, it sometimes lacks clarity in its terminology. New terms have been defined by various authors to reduce ambiguity or replace existing problematic terms. To make these new terms, but also terms used by the International Code of Zoological Nomenclature (the Code), accessible, an electronic Simple Knowledge Organization System (SKOS) thesaurus was created, called Zoonom. Zoonom was built using an open-source thesaurus-making software, Opentheso. Opentheso complies with the most recent standards i.e., ISO 25964-1 (International Organization for Standardization 2011) and ISO 25964-2 (International Organization for Standardization 2013). The thesaurus is shared online through the LOTERRE platform (Linked Open TERminology REssources). SKOS is part of the Semantic Web family of standards and a World Wide Web Consortium (W3C) recommendation for controlled vocabularies and thesauri. It is itself based on the Web Ontology Language (OWL). See some applications of SKOS and semantic web for biodiversity in Larmande et al. 2013. Zoonom contains 920 terms (excluding terms from the same word families, like plurals), distributed within 794 concepts, 404 etymologies and 58 references. It is divided into 20 collections and covers every aspect of zoological nomenclature, from theoretical nomenclature to taxonomic publications. Find the link to a downloadable file in the description of Zoonom. The thesaurus can be used as a classical glossary, using the search bar, or in alphabetical order, but this is not its only feature. Gathering different terms under a single concept also offers the possibility of refining the terminologies, and thus accessing a less ambiguous equivalent term. A richly developed vocabulary enables better labeling of particular names or situations in databases, software, or in the context of Semantic Web. As an example, let’s focus on the concept of nomen dubium. It is defined by the Code as “a name of unknown or doubtful application” (International Commission on Zoological Nomenclature 1999). However, at least four different major categories of nomina dubia exist. Names attached to multiple types belonging to potentially different taxa; names attached to a problematic type; names attached to a non-identifiable type; and names not clearly available because their conditions of availability have not been checked. Concepts have been created to distinguish each of these situations: Synaptonym, Anaptonym, Nyctonym (Dubois 2011) and Aporioplonym. The nomenclatural treatment of these names varies. Some may need the designation of a neotype (nyctonym) or if relevant, lectotype (synaptonym); others may need a referral to the Commission, while some will stay dubious, or even end up being deemed unavailable (aporioplonym). The simple tagging “nomen dubium” gives little to no information about the exact status of the name, only implying that it is not valid. A better description of the scientific names in databases is beneficial both for information retrieval and intercommunication. Zoonom is destined to be updated at least once a year. Any relevant propositions of new concepts are highly welcomed. We are especially looking for terms widely used in a part of the taxonomic community, or associated with a particular taxon, but unknown or obscure outside of these applications. Crosslinking the common concepts with the NOMEN OWL ontology (Yoder et al. 2017) and Wikidata might be implimented in the near future. In conclusion, Zoonom should help provide a better understanding of zoological nomenclature and assist in the curation and management of databases by offering improved tags and definitions.


2021 ◽  
Vol 11 (17) ◽  
pp. 7782
Author(s):  
Itziar Urbieta ◽  
Marcos Nieto ◽  
Mikel García ◽  
Oihana Otaegui

Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.


2021 ◽  
Author(s):  
Kimia Zandbiglari ◽  
Farhad Ameri ◽  
Mohammad Javadi

Abstract The unstructured data available on the websites of manufacturing suppliers can provide useful insights into the technological and organizational capabilities of manufacturers. However, since the data is often represented in an unstructured form using natural language text, it is difficult to efficiently search and analyze the capability data and learn from it. The objective of this work is to propose a set of text analytics techniques to enable automated classification and ranking of suppliers based on their capability narratives. The supervised classification and semantic similarity measurement methods used in this research are supported by a formal thesaurus that uses SKOS (Simple Knowledge Organization System) for its syntax and semantics. Normalized Google Distance (NGD) was used as a metric for measuring the relatedness of terms. The proposed framework was validated experimentally using a hypothetical search scenario. The results indicate that the generated ranked list shows a high correlation with human judgment specially if the query concept vector and supplier concept vector belong to the same class. However, the correlation decreases when multiple overlapping classes of suppliers are mixed together. The findings of this research can be used to improve the precision and reliability of Capability Language Processing (CLP) tools and methods.


Author(s):  
Sandro Rautenberg ◽  
Lucélia de Souza ◽  
João Pedro Kelniar

Objetivo: Considera-se a Organização de Conhecimento como atividade interdisciplinar da Ciência da Informação e da Ciência da Computação que possibilita a representação de elementos de conhecimento em ambientes baseados em Web Semântica. Em face da interdisciplinaridade, o artigo apresenta as escolhas e os esforços despendidos no desenvolvimento de um Tesauro da Ciência da Computação. Método: como pesquisa aplicada, um processo de desenvolvimento de ontologias é utilizado, ao considerar que ontologias e tesauros são Sistemas de Organização de Conhecimento com atividades correlacionadas. Resultado: baseado no modelo da Web Semântica Simple Knowledge Organization System – SKOS, o tesauro é publicado na Web de Dados e pode ser acessado a partir do endpoint http://lod.unicentro.br/sparql. Admite-se que o público-alvo do Sistema de Organização de Conhecimento desenvolvido são profissionais, professores, pesquisadores, alunos de graduação/pós-graduação, sendo o tesauro um subsídio à comunicação científica entre os referidos atores. Conclusões: observa-se que os processos de desenvolvimento de ontologias podem ser empregados para o desenvolvimento de tesauros. Ademais, o uso do SKOS como modelo para o desenvolvimento de tesauros mostrou-se adequado, conforme as premissas de organização e representação de recursos digitais do referido modelo.


Author(s):  
Mark R. Stöhr ◽  
Andreas Günther ◽  
Raphael W. Majeed

Metadata repositories are an indispensable component of data integration infrastructures and support semantic interoperability between knowledge organization systems. Standards for metadata representation like the ISO/IEC 11179 as well as the Resource Description Framework (RDF) and the Simple Knowledge Organization System (SKOS) by the World Wide Web Consortium were published to ensure metadata interoperability, maintainability and sustainability. The FAIR guidelines were composed to explicate those aspects in four principles divided in fifteen sub-principles. The ISO/IEC 21526 standard extends the 11179 standard for the domain of health care and mandates that SKOS be used for certain scenarios. In medical informatics, the composition of health care SKOS classification schemes is often managed by documentalists and data scientists. They use editors, which support them in producing comprehensive and valid metadata. Current metadata editors either do not properly support the SKOS resource annotations, require server applications or make use of additional databases for metadata storage. These characteristics are contrary to the application independency and versatility of raw Unicode SKOS files, e.g. the custom text arrangement, extensibility or copy & paste editing. We provide an application that adds navigation, auto completion and validity check capabilities on top of a regular Unicode text editor.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcin Roszkowski ◽  
Bartłomiej Włodarczyk

PurposeThe paper aims to present the development of conceptualization of coronavirus disease 2019 (COVID-19) based on associations with other articles on English edition of Wikipedia. The main goal of the paper is to study the social organization of knowledge about COVID-19 within the Wikipedia community of practice.Design/methodology/approachThe methodological approach taken in this study was based on the application of Moscovici's theory of social representations to Wikipedia's knowledge organization system (KOS). Internal links in the Wikipedia article about COVID-19 were considered anchors in its social representations. Each link in the introductory part of the article was considered an indicator of the semantic relationship between COVID-19 and other concepts from Wikipedia's knowledge base. The subject of this study was links extracted from all revisions of the COVID-19 article between February and September 2020. Qualitative and quantitative analyses were performed on these conceptual structures using both synchronic and diachronic approaches.FindingsIt was found that the evolution of anchors in the Wikipedia article on COVID-19 was in line with the mechanism of symbolic coping related to infectious disease. It went through stages of divergence, convergence and normalization. It shows that this mechanism governs the social organization of knowledge related to COVID-19 on Wikipedia.Originality/valueNo studies have been devoted to the image of COVID-19 as presented by the evolution of links in Wikipedia and its implications for knowledge organization (KO).


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