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Based on sample evaluation, this article investigates didactic actions and the display of expertise within the field of popular music-related online tutorials distributed on YouTube. In a first step, didactic activity and musical expertise are discussed with reference to institutional, interactional and media aspects in order to gain a comprehensive image of what influences the teaching of popular music. By means of the sample evaluation – the sample concentrates on highly-requested YouTube channels – basic insights into the specificity of the didactic situation induced by popular music-related online tutorials are to be provided. The analysis pays particular attention to the interlacing of knowledge organization, performance and audio-visual production. Overall, it can be shown how specific rules regarding the representation and gratification of expertise emerge in the YouTube media environment, examples of which are the “professional” appearance of the instructors and the compartmentalization of popular music-related cultural knowledge.

The goal of any library is to ensure high quality and general availability of information retrieval tools. The paper describes the project implemented by the Russian State Library (RSL) to present Library Bibliographic Classification as a Networked Knowledge Organization System. The project goal is to support content and provide tools for ensuring system’s interoperability with other resources of the same nature (i.e. with Linked Data Vocabularies) in the global network environment. The project was partially supported by the Russian Foundation for Basic Research (RFBR).The RSL General Classified Catalogue (GCC) was selected as the main data source for the Classification system of knowledge organization. The meaning of each classification number is expressed by complete string of wordings (captions), rather than the last level caption alone. Data converted to the Resource Description Framework (RDF) files based on the standard set of properties defined in the Simple Knowledge Organization System (SKOS) model was loaded into the semantic storage for subsequent data processing using the SPARQL query language. In order to enrich user queries for search of resources, the RSL has published its Classification System in the form of Linked Open Data (https://lod.rsl.ru) for searching in the RSL electronic catalogue. Currently, the work is underway to enable its smooth integration with other LOD vocabularies. The SKOS mapping tags are used to differentiate the types of connections between SKOS elements (concepts) existing in different concept schemes, for example, UDC, MeSH, authority data.The conceptual schemes of the leading classifications are fundamentally different from each other. Establishing correspondence between concepts is possible only on the basis of lexical and structural analysis to compute the concept similarity as a combination of attributes.The authors are looking forward to working with libraries in Russia and other countries to create a common space of Linked Open Data vocabularies.

As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs.

Purpose With more and more digital collections of various information resources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC. Design/methodology/approach State-of-the-art machine learning algorithms require at least 1,000 training examples per class. The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data (totaling 802 classes in the training and testing sample, out of 14,413 classes at all levels). Findings Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average; the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task. Word embeddings combined with different types of neural networks (simple linear network, standard neural network, 1D convolutional neural network, and recurrent neural network) produced worse results than Support Vector Machine, but reach close results, with the benefit of a smaller representation size. Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input. Stemming only marginally improves the results. Removed stop-words reduced accuracy in most cases, while removing less frequent words increased it marginally. The greatest impact is produced by the number of training examples: 81.90% accuracy on the training set is achieved when at least 1,000 records per class are available in the training set, and 66.13% when too few records (often less than 100 per class) on which to train are available—and these hold only for top 3 hierarchical levels (803 instead of 14,413 classes). Research limitations Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes, skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems. Practical implications In conclusion, for operative information retrieval systems applying purely automatic DDC does not work, either using machine learning (because of the lack of training data for the large number of DDC classes) or using string-matching algorithm (because DDC characteristics perform well for automatic classification only in a small number of classes). Over time, more training examples may become available, and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC. In order for quality information services to reach the objective of highest possible precision and recall, automatic classification should never be implemented on its own; instead, machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future. Originality/value The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems. Due to lack of sufficient training data across the entire set of classes, an approach complementing machine learning, that of string matching, was applied. This combination should be explored further since it provides the potential for real-life applications with large target classification systems.

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.

This article assumes that the tools used for knowledge organization purposes (e.g., thesauri, classification schemes…) may be understood as terminological resources. We investigate the relationship between terminology and knowledge organization from the double-dimension perspective (both linguistic and conceptual) of terminology as a field of study. Then, we propose an analysis of the concepts underlying designations such as ‘documentary language’, ‘controlled vocabulary’ and ‘knowledge organization system’ in specialized texts. We conclude with an overview of SKOS (Simple Knowledge Organization System), a model to represent knowledge organization systems in the semantic web, which is evaluated in terms of its ability to model terminological resources according to the double-dimension approach and the main elements of the ISO 1087 standard. 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.

This research was aimed at constructing a thesaurus of the ethnic groups in the Mekong River Basin that is a compilation of controlled vocabularies of both Thai and English language, with a digital platform that enables semantic search and linked open data. The research method involved four steps: (1) organization of knowledge content, (2) construction of the thesaurus, (3) development of a digital thesaurus platform, and (4) evaluation. The concepts and theories used in the research comprised knowledge organization, thesaurus construction, digital platform development, and system evaluation. The tool for developing the digital thesaurus was the Tematres web application. The research results are: (1) there are 4273 principle words related to the ethnic groups that have been compiled and classified by the terms for each of the eight deep levels, 2596 were found to have hierarchical relationships, and 6858 had associative relationships, (2) the digital thesaurus platform was able to manage the controlled vocabularies related to the Mekong ethnic groups by storing both Thai and English vocabularies. When retrieved, the vocabulary, details of the broader term, narrow term, related term, cross reference, and scope note are displayed. Thus, semantic search is viable through applications, linked open data technology, and web services.

PurposeThe purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization.Design/methodology/approachThe deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science.FindingsThe MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search.Research limitations/implicationsThe use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users.Practical implicationsThe MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches.Social implicationsThe MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users.Originality/valueThe MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.

This comparative study investigates the use of and preference towards the two major academic library classification schemes – Dewey Decimal Classification (DDC) and Library of Congress Clas...

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.

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.

RESUMO Na literatura, existem palavras que apresentam mais de um significado nos múltiplos contextos, e isso as faz terem interpretações ambíguas ou mesmo serem consideradas polissêmicas; é o caso da palavra classificação. Nas diversas acepções da palavra, ela pode significar um processo, um sistema de classificação ou uma disciplina. Este artigo tem como objetivo estudar a perspectiva interdisciplinar do significado da palavra classificação, a partir das acepções sugeridas pelos autores da área, com o intuito de traçar sua evolução e contexto histórico. Utiliza-se a base metodológica proposta por Bardin (2011) sobre Análise de conteúdo, aplicada nas 40 definições selecionadas a partir do artigo “Classification” (Hjørland, 2017), como amostra da pesquisa. Os resultados das análises léxicas e categoriais das definições confirmaram os pressupostos de que a classificação traça sua história pelas bases categoriais aristotélicas, tendo começado seu desenvolvimento dicotômico com os estudos de Porfírio, ganhado projeção com o sistema de categorias taxonômicas de Lineu, com a ideia de “divisão e denominação” para classificar os seres em grupos, com a obra A Origem das espécies de Darwin; e, no âmbito da Biblioteconomia e Ciência da Informação, com os estudos iniciados pelo bibliotecário Richards (1860-1939), no século XIX, influenciando os estudos de autores como Sayers (1881-1960), Bliss (1870-1955) e. Ranganathan (1892-1972), entre outros. Na área da Biblioteconomia e Ciência da Informação, a classificação tem sido estudada como uma disciplina, um processo de agrupar e ordenar o conhecimento, e como instrumento de representação da informação, contribuindo para a sua organização e recuperação. ABSTRACT In the literature, words have more than one meaning in multiple contexts, and this makes them have ambiguous interpretations or even be considered polysemic; this is the case with the word classification. In the different meanings of the word, it can mean a process, a classification system or a discipline. This article studies the interdisciplinary perspective of the meaning of the word classification, from the meanings suggested by the authors of the area, to trace its evolution and historical context. We use the methodological basis proposed by Bardin (2011) on Content Analysis, applied to the 40 definitions selected from the article “Classification” (Hjørland, 2017), as a research sample. The results of lexical and categorical analysis of the definitions confirmed the assumptions that the classification traces its history based on the Aristotelian categorical bases, having started its dichotomous development with the studies of Porphyrios, gaining projection with the system of taxonomic categories of Linnaeus, with the idea of “Division and denomination” to classify beings into groups, with Darwin's Origin of Species; and, in the scope of Librarianship and Information Science, with the studies initiated by librarian Richards (1860-1939), in the 19th century, influencing the studies of authors such as Sayers (1881-1960), Bliss (1870-1955) e. Ranganathan (1892-1972), among others. In the area of Librarianship and Information Science, classification has been studied as a discipline, a process of grouping and ordering knowledge, and as an instrument of information representation, contributing to its organization and recovery.

Resource description framework (RDF) is used to represent resources from different sources. Although initially used as a knowledge representation framework for the semantic web, it can be used for the knowledge representation in knowledge-based systems. There are several advantages to using RDF for knowledge representation in a knowledge-based system. Moreover, the RDF is used in Simple Knowledge Organization which can be used to organize knowledge in a knowledge-based system. The semantic knowledge graph, which combines the RDF knowledge graph with the simple knowledge organization and link resources to the unstructured data, is a good knowledge representation structure for a knowledge-based system. A good example of an application using the knowledge-based system is the Clinical Decision Support System. The chapter explains how the knowledge representation using RDF-based semantic knowledge graph in a knowledge-based clinical decision support system increases the efficiency and reliability of the system.

Most libraries put a lot of effort into developing subject headings or thesauri, which are used to index and retrieve information. Nevertheless, in the library field, controlled vocabularies are associated to authority records as authority files. In order to become findable by search engines, these authority files should be modelled on semantic vocabularies. This research proposes an authority-record conversion process for publishing thesauri and subject headings as linked data, by using the Simple Knowledge Organization Systems data model. To this purpose, we undertook a bibliographic and documentary research on the World Wide Web Consortium recommendation guidelines, which were used to produce a set of procedures and technologies to support the conversion proposal. This research provides evidences that controlled vocabularies are an important resource for improving information retrieval on the web. The proposed conversion process works as a quick guide for controlled vocabulary integration and reuse among users and systems on the linked data environment. Although the proposal was originally intended for a library setting, it can be applied and tested in another type of institution, such as documentation centres, museums, or cultural heritage archives. It can also be used in other linked open data projects. Resumo Grande parte das bibliotecas concentram esforços em desenvolver cabeçalhos de assuntos ou tesauros, os quais são usados para indexar e para recuperar informações. No entanto, no campo das bibliotecas, os vocabulários controlados são associados aos registros bibliográficos como arquivos de autoridade. Para se tornarem localizáveis pelos mecanismos de pesquisa, esses registros de autoridade devem ser modelados em vocabulários semânticos. Esta pesquisa propõe um processo de conversão de registros de autoridades para a publicação de tesauros e de cabeçalhos de assuntos como dados abertos conectados, utilizando o modelo de dados Simple Knowledge Organization Systems. Para tanto, realizou-se uma pesquisa bibliográfica e documental sobre as diretrizes e a recomendação do World Wide Web Consortium, as quais foram usadas para produzir um conjunto de procedimentos e de tecnologias para apoiar a proposta de conversão. Este trabalho fornece evidências de que os vocabulários controlados são um recurso importante para melhorar a recuperação de informações na web. O processo de conversão proposto funciona como um guia rápido para a integração e a reutilização dos vocabulários controlados entre usuários e sistemas no ambiente de dados abertos conectados. Embora a proposta tenha sido originalmente destinada à realidade das bibliotecas, pode ser aplicada e testada em instituições de natureza diversificada, como centros de documentação, museus ou arquivos. Ela também pode ser usada em outros projetos de dados abertos conectados.

Taxonomies play a central role in conceptual domain modeling having a direct impact in areas such as knowledge representation, ontology engineering, software engineering, as well as in knowledge organization in information sciences. Despite their key role, there is in the literature little guidance on how to build high-quality taxonomies, with notable exceptions such as the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules for forming taxonomic structures. In this paper, we show how to leverage on the formal rules underlying these techniques to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. To validate our proposal, we formalize these operations as a graph grammar. Moreover, to demonstrate our claim of correctness by construction, we use automatic verification techniques over the grammar language to show that: (i) all taxonomies produced by the grammar rules are correct; and (ii) the rules can generate all correct taxonomies.

The paper describes the creation of a vocabulary for a domain-specific information service platform (SIS move) by vocabulary re-use and linking. Source vocabularies differ with respect to several factors (domain-specificity, accessibility, data model). We address why vocabularies should be considered for a domain-specific vocabulary and how they are brought under a common modelling paradigm with standards for knowledge organization systems and alignment of schemata. We also discuss the creation and validation of alignments. Eventually, we give an outlook on the vocabulary’s further evolution and application.

Ding, L., 2020. Research on semantic analysis of ocean flow field based on feature similarity collaborative filtering. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 53–57. Coconut Creek (Florida), ISSN 0749-0208.In the field of ocean space-time data modeling research, fields and features are usually used to describe and organize ocean current phenomena, which is continuous and dynamic. However, during the process, the semantic analysis and expression of ocean flow field phenomena often produce heterogeneous problems. Because the feature theory can construct and define the conceptual model framework of domain knowledge system, the semantic analysis of the ocean flow field using the feature theory can be regarded as one of the ways to achieve the semantic sharing of the ocean flow field. Moreover, based on the collection and analysis of existing knowledge and concepts in the field of marine science, a quadruple-based organization in the paper is proposed for ocean flow field features, in which by expanding the concept features, attribute features, and relationship features, a basic structure-based approach is formed. The overall characteristic structure of the ocean flow field comprises concept, attribute, and relationship structure. Additionally, collaborative filtering is performed on the similarity of the structural features in the ocean flow field, and a formal grammatical expression system conforming to the characteristics of continuous flow field is also made so that a normative framework system is provided for the ocean flow field knowledge organization. The OWL language is used, which allows for the conduction of the formal expression and description of geostrophic flow, which provides semantic support for the integration and management of spatiotemporal data in the ocean flow fields.

Background How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may extract such biomedical facts, although the state-of-the-art focuses on pair-based proportional (pairwise) analogies such as man:woman::king:queen (“queen = −man +king +woman”). Objective This study aimed to systematically extract disease treatment statements with a Semantic Deep Learning (SemDeep) approach underpinned by prior knowledge and another type of 4-term analogy (other than pairwise). Methods As preliminaries, we investigated Continuous Bag-of-Words (CBOW) embedding analogies in a common-English corpus with five lines of text and observed a type of 4-term analogy (not pairwise) applying the 3CosAdd formula and relating the semantic fields person and death: “dagger = −Romeo +die +died” (search query: −Romeo +die +died). Our SemDeep approach worked with pre-existing items of knowledge (what is known) to make inferences sanctioned by a 4-term analogy (search query −x +z1 +z2) from CBOW and Skip-gram embeddings created with a PubMed systematic reviews subset (PMSB dataset). Stage1: Knowledge acquisition. Obtaining a set of terms, candidate y, from embeddings using vector arithmetic. Some n-gram pairs from the cosine and validated with evidence (prior knowledge) are the input for the 3cosAdd, seeking a type of 4-term analogy relating the semantic fields disease and treatment. Stage 2: Knowledge organization. Identification of candidates sanctioned by the analogy belonging to the semantic field treatment and mapping these candidates to unified medical language system Metathesaurus concepts with MetaMap. A concept pair is a brief disease treatment statement (biomedical fact). Stage 3: Knowledge validation. An evidence-based evaluation followed by human validation of biomedical facts potentially useful for clinicians. Results We obtained 5352 n-gram pairs from 446 search queries by applying the 3CosAdd. The microaveraging performance of MetaMap for candidate y belonging to the semantic field treatment was F-measure=80.00% (precision=77.00%, recall=83.25%). We developed an empirical heuristic with some predictive power for clinical winners, that is, search queries bringing candidate y with evidence of a therapeutic intent for target disease x. The search queries -asthma +inhaled_corticosteroids +inhaled_corticosteroid and -epilepsy +valproate +antiepileptic_drug were clinical winners, finding eight evidence-based beneficial treatments. Conclusions Extracting treatments with therapeutic intent by analogical reasoning from embeddings (423K n-grams from the PMSB dataset) is an ambitious goal. Our SemDeep approach is knowledge-based, underpinned by embedding analogies that exploit prior knowledge. Biomedical facts from embedding analogies (4-term type, not pairwise) are potentially useful for clinicians. The heuristic offers a practical way to discover beneficial treatments for well-known diseases. Learning from deep learning models does not require a massive amount of data. Embedding analogies are not limited to pairwise analogies; hence, analogical reasoning with embeddings is underexploited.

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.

This book provides a complete introduction to the rapidly expanding field of Knowledge organization (KO), presenting historical precedents and theoretical foundations in a discursive, intelligible form, covering the philosophical, linguistic and technical aspects. In the contemporary context of global information exchange through linked data, Knowledge organization systems (KOS) need to be represented in standard inter-operable formats. Different formats for KOS representation including MARC, Dublin Core, SKOS and OWL are introduced as well as the application of Knowledge organization to a variety of activities and contexts: education, encyclopedic knowledge, the Internet, libraries, archives, museums, galleries and other institutions collecting and providing access to recorded knowledge. Key coverage includes: - ontology and epistemology in KO- KO structures: lists, hierarchies, facets...- KO types: tagging, taxonomies, thesauri, classifications...- conceptual analysis of documents- applications in the digital age. Covering theoretical and practical aspects of KO and using real-life examples to illustrate its application, this book will be a valuable resource for students, researchers and practitioners of Knowledge organization, information organization, cataloguing and classification.

International audience; Recently, there is a need to move knowledge organization systems (KOS) to online applications, by using Semantic Web technologies, in order to optimize indexing and searching. The present paper reports the representation of the Integrative Levels Classification (ILC) as a networked KOS, through conversion of its second edition into the W3C standard SKOS (Simple Knowledge Organization System) format.

Producción Científica, Thesauri are Knowledge Organization Systems (KOS), that arise from the consensus of wide communities. They have been in use for many years and are regularly updated. Whereas in the past thesauri were designed for information professionals for indexing and searching, today there is a demand for conceptual vocabularies that enable inferencing by machines. The development of the Semantic Web has brought a new opportunity for thesauri, but thesauri also face the challenge of proving that they add value to it. The evolution of thesauri toward their integration with the Semantic Web is examined. Elements and structures in the thesaurus standard, ISO 25964, and SKOS (Simple Knowledge Organization System), the Semantic Web standard for representing KOS, are reviewed and compared. Moreover, the integrity rules of thesauri are contrasted with the axioms of SKOS. How SKOS has been applied to represent some real thesauri is taken into account. Three thesauri are chosen for this aim:AGROVOC, EuroVoc and the UNESCO Thesaurus. Based on the results of this comparison and analysis, the bene ts that Semantic Web technologies offer to thesauri, how thesauri can contribute to the Semantic Web, and the challenges that would help to improve their integration with the Semantic Web are discussed.

Astronomy classification is often overlooked in classification discourse. Its rarity and obscurity, especially within UK librarianship, suggests it is an underdeveloped strand of classification research and is possibly, undervalued in modern librarianship. The purpose of this research is to investigate the suitability and practicalities of the discipline of astronomy adopting a subject-specific faceted classification scheme and to provide a provisional outline of a special faceted astronomy classification scheme. The research demonstrates that the application of universal schemes for astronomy classification had left the interdisciplinary subject ill catered for and outdated; making accurate classification difficult for specialist astronomy collections. A faceted approach to classification development is supported by two qualitative literature-based research methods; historical research into astronomy classification and an analytico-synthetic classification case study. The subsequent classification development is influenced through a pragmatic and scholarly-scientific approach and constructed by means of instruction from faceted classification guides by Vickery (1960) and Batley (2005), and faceted classification principles from Ranaganathan (1937). This research fills a gap within classification discourse on specialist interdisciplinary subjects, specifically within astronomy and demonstrates the best means for their classification. It provides a means of assessing further the value of faceted classification within astronomy librarianship.

Terminological resources play a central role in the organization and retrieval of scientific texts. Both simple keyword lists and advanced modelings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the web or within local repositories. This seems especially true for long-established scientific fields with elusive theoretical and historical branches, where the use of terminology within documents from different origins is often far from being consistent. In this paper, we report on the progress of a linguistically motivated project on the onomasiological re-modeling of the terminological resources for the grammatical information system grammis. We present the design principles and the results of their application. In particular, we focus on new features for the authoring backend and discuss how these innovations help to evaluate existing, loosely structured terminological content, as well as to efficiently deal with automatic term extraction. Furthermore, we introduce a transformation to a future SKOS representation. We conclude with a positioning of our resources with regard to the Knowledge Organization discourse and discuss how a highly complex information environment like grammis benefits from the re-designed terminological KOS.

If scientific research can be described as "arm's length'' cooperative work, then the sciences engaged in the creation and maintenance of cooperatively built classification systems might be said to require extremely long arms; workers must reach across great distances of time, space and awareness to independently contribute to the creation and maintenance of a conceptual infrastructure. This is particularly the case for the field of biological taxonomy, in which researchers across the globe have, for more than three hundred years, cooperatively developed a massive knowledge organization system describing all life on earth. As taxonomists move from paper-based workflows to born-digital, data-first modes of publishing, new tools and approaches are needed to support their work. In this paper, we present research conducted as part of the "Transforming Taxonomic Interfaces" project aimed at improving interfaces for taxonomic software. We describe biological taxonomy in the context of CSCW, and identify key strategies taxonomists deploy to facilitate loosely coupled cooperative work. We also contribute a discussion of semantic refactoring, a unique kind of "articulation work'' entailed in developing, maintaining, and migrating classification systems. This work has implications for the design and study of knowledge organization systems and infrastructure, classification research, and for the development of general semantic web tools and software as well as those specifically for biological taxonomy.

Purpose The common understanding of generalization/specialization relations assumes the relation to be equally strong between a classifier and any of its related classifiers and also at every level of the hierarchy. Assigning a grade of relative distance to represent the level of similarity between the related pairs of classifiers could correct this situation, which has been considered as an oversimplification of the psychological account of the real-world relations. The paper aims to discuss these issues. Design/methodology/approach The evaluation followed an end-user perspective. In order to obtain a consistent data set of specialization distances, a group of 21 persons was asked to assign values to a set of relations from a selection of terms from the AGROVOC thesaurus. Then two sets of representations of the relations between the terms were built, one according to the calculated concept of specialization weights and the other one following the original order of the thesaurus. In total, 40 persons were asked to choose between the two sets following an A/B test-like experiment. Finally, short interviews were carried out after the test to inquiry about their decisions. Findings The results show that the use of this information could be a valuable tool for search and information retrieval purposes and for the visual representation of knowledge organization systems (KOS). Furthermore, the methodology followed in the study turned out to be useful for detecting inconsistencies in the thesaurus and could thus be used for quality control and optimization of the hierarchical relations. Originality/value The use of this relative distance information, namely, “concept specialization distance,” has been proposed mainly at a theoretical level. In the current experiment, the authors evaluate the potential use of this information from an end-user perspective, not only for text-based interfaces but also its application for the visual representation of KOS. Finally, the methodology followed for the elaboration of the concept specialization distance data set showed potential for detecting possible inconsistencies in KOS.

Computer-supported collaborative learning (CSCL) environments provide learners with multiple representational tools for storing, sharing, and constructing knowledge. However, little is known about ...

Patents are usually organized in classes generated by the offices responsible for patents protection, to create a useful format to the information retrieval process. The complexity of patent taxonomies is a challenge for the automation of patent classification. Beside this, the high numbers of subgroups makes the classification in deeper levels more difficult. This work proposes a method to cluster patents using Self Organizing Maps (SOM) networks and bibliographic coupling. To validate the proposed method, an empirical experiment used a patent database from a specific classification system. The obtained results show that patents clusters were successfully identified by SOM through their cited references, and that SOM results were similar to k-Means algorithm results to perform this task. This study can contribute to the development of the knowledge organization systems by evaluating the use of citation analysis in the automatic clustering of patents in a constrained knowledge domain, at the subgroup level of current patent classification systems.

The intelligent maintenance of power plants greatly relies upon previous experiences and historical information to make informed decisions. Previous research is predominantly focused on collecting data from the maintenance process while little work has been done on the automatic capture and mining of knowledge resources from the data accumulated. Focusing on the experience feedback issue in power plants maintenance, this work proposes a novel process of automatic construction and reasoning of knowledge graphs to support the intelligent maintenance of complex power equipment. In this process, the Bi-LSTM-CRF model and the attention-based Bi-LSTM are specifically used to identify and extract entities and relations from unstructured status reports. On this basis, the knowledge graph construction method based on the neo4j graph database is developed. This paper details the preliminary work towards implementing the proposed process.

This paper takes the creation of knowledge graph of the Hundred Schools of Thought an example to discuss the application value and realization path of knowledge graph in knowledge organization of digital humanities. We have formed the idea of establishing the knowledge graph of the Hundred Schools of Thought, which consists of four steps: knowledge representation, knowledge extraction, knowledge storage, semantic search and knowledge visualization. At present, this paper has completed the ontology construction of Confucius, Laozi and Mozi, which are the three core figures of the Hundred Schools of Thought. This knowledge graph will be further realized by using web crawler and neo4j.

Niniejszy artykuł podejmuje próbę odpowiedzi na pytanie, czym jest hipertekst, z którym mamy do czynienia na co dzień dzięki technologiom cyfrowym. W tym celu autor dokonuje analizy wybranej literatury na temat hipertekstu i rozpatruje rozumienie pojęcia w odniesieniu do tekstu linearnego obecnego w książkach, do internetu, do cybertekstu, a także jako formę zarządzania wiedzą. Szczególne miejsce zostaje poświęcone badaniom G. Landowa, który od wielu lat zajmuje się tym zagadnieniem,opisuje je i ukazuje jego wpływ na nową jakość czytania, a co za tym idzie, na dydaktykę, która dużo miejsca poświęca na pracę z tekstem (podręczniki). Kwestia wpływu na dydaktykę pozostała jedynie zasygnalizowana i niepogłębiona.Hipertekst jest formą tekstu zapisanego w sposób nielinearny i swoją strukturą przypomina formę sieci, w której zasadniczą rolę odgrywa link, czyli łącznik pomiędzy węzłami, które stanowią bloki tekstowe, a także informacje zapisane w postaci audio-wizualnej. Przykładem hipertekstu jest internet, gdzie odnajdujemy podobną strukturę oraz wymienione elementy. Hipertekst odgrywa znaczącą rolę w organizacji wiedzy; dzięki obecności hiperłączy umożliwia szybkie wyszukanie i dotarcie do informacji, co ma szczególne znaczenie w przypadku dużej jej ilości, a co byłoby utrudnione w przypadku posługiwania się jedynie metodą linearną.Powyższe opracowanie zachęca do pogłębienia tematu w celu zrozumienia wpływu hipertekstu na kształtowanie się kultury.

Subject Ontologies represent conceptualizations of disciplinary domains in which concepts symbolize topics that are relevant for the considered domain and are associated each other by means of specific relations. Usually, these kind of lightweight ontologies are adopted in knowledge-based educational environments to enable semantic organization and search of resources and, in other cases, to support personalization and adaptation features for learning and teaching experiences. For this reason, applying effective management methodologies for Subject Ontologies is a crucial aspect in engineering the environments. In particular, this paper proposes an approach to use SKOS (a Semantic Web-based vocabulary providing a standard way to represent knowledge organization systems) for modelling subject ontologies. Moreover, the paper underlines the main benefits of SKOS. It focuses on alternative strategies for storing and accessing ontologies in order to support the knowledge sharing, knowledge reusing, planning, assessment, customization and adaptation processes related to learning scenarios. The results of an early experimentation allowed the authors defining a framework able to support, from both methodological and technological viewpoints, the use of Subject Ontologies in the context of a Semantic Web-based Educational System. The defined framework has high performances in terms of response and this may really improve the user experience. We defined a framework to use Subject Ontologies in Educational Systems.Subject Ontologies support learning scenarios by representing themes to treat.SKOS is a good technical approach to improve the learning experience of users.We identified a good strategy to treat Subject Ontologies and support SWBESs.The described approach may improve the performance of the learning processes.

The growth of virtual reality applications in recent years has facilitated the active participation of people at the level of interactivity in games, simulations and mainly entertainment aspects. In educational environments, it is no exception and there are initiatives for the interaction of students with learning materials within immersive spaces. However, within this educational area, there are few studies relating to effective methods of navigation to access resources. The following study presents a proposal for Navigational search for resources in virtual reality based on a Knowledge Organization System (KOS), using as a case study topics associated with a zoo. The main objective is to analyze the association of concepts in a 3D navigation structure, and basic aspects of usability through the use of mobile devices

The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of main concern the need to clarify the URIs' ontological meaning. The aim of this study is to investigate the relation between the ontological substance of KOS concepts and concepts revealed through the grammatical and syntactic formalisms of natural language. For this purpose, we examined the dividableness of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a classification scheme) by applying Natural Language Processing (NLP) techniques (i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF literals) of SKOS concepts. The results of the comparative analysis reveal that, despite the use of multi-word units, thesauri tend to represent concepts in a way that can hardly be further divided conceptually, while Subject Headings and Classification Schemes - to a certain extent - comprise terms that can be decomposed into more conceptual constituents. Consequently, SKOS concepts deriving from thesauri are more likely to represent atomic conceptual units and thus be more appropriate tools for inference and reasoning. Since identifiers represent the meaning of a concept, complex concepts are neither the most appropriate nor the most efficient way of modelling a KOS for the Semantic Web., Comment: 18 pages, 5 tables

The Web is the most used Internet's service to create and share information. In large information collections, Knowledge Organization plays a key role in order to classify and to find valuable information. Likewise, Linked Open Data is a powerful approach for linking different Web datasets. Today, several Knowledge Organization Systems are published by using the design criteria of linked data, it facilitates the automatic processing of them. In this paper, we address the issue of traversing open Knowledge Organization Systems, considering difficulties associated with their dynamics and size. To fill this issue, we propose a method to identify irrelevant nodes on an open graph, thus reducing the time and the scope of the graph path and maximizing the possibilities of finding more relevant results. The approach for graph reduction is independent of the domain or task for which the open system will be used. The preliminary results of the proof of concept lead us to think that the method can be effective when the coverage of the concept of interest increases.

This paper explains the connection and mapping of knowledge representations between RDF and CMDI. Therefore, the challenge is to create a bridge between Linked Open Data (LOD) and the Component MetaData Infrastructure (CMDI) to ensure that the limits of the two paradigms are compensated and strengthened to create a new hybrid approach. While on the one hand, CMDI is easier to use for modelling purposes, the Metadata is not descriptive enough for a document to be easily discoverable using Linked Data (LD) technologies to publish and to enrich the document’s content. Yet on the other hand, the explicit semantics and high interoperability of LOD have many advantages, but its modelling process is too complex for non-expert users. Here we show how knowledge organization plays a crucial role in this issue.

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study language, social behavior, knowledge organization, and network theory. While being a scientific treasure, the large size of the dataset hinders pre-processing and may be a challenging obstacle for potential new studies. This issue is particularly acute in scientific domains where researchers may not be technically and data processing savvy. On one hand, the size of Wikipedia dumps is large. It makes the parsing and extraction of relevant information cumbersome. On the other hand, the API is straightforward to use but restricted to a relatively small number of requests. The middle ground is at the mesoscopic scale when researchers need a subset of Wikipedia ranging from thousands to hundreds of thousands of pages but there exists no efficient solution at this scale. In this work, we propose an efficient data structure to make requests and access subnetworks of Wikipedia pages and categories. We provide convenient tools for accessing and filtering viewership statistics or "pagecounts" of Wikipedia web pages. The dataset organization leverages principles of graph databases that allows rapid and intuitive access to subgraphs of Wikipedia articles and categories. The dataset and deployment guidelines are available on the LTS2 website \url{https://lts2.epfl.ch/Datasets/Wikipedia/}.

In this article, the Basel Register of Thesauri, Ontologies & Classications (BARTOC.org) is introduced to raise awareness for an integrated, full terminology registry for knowledge organization systems. Recently, researchers have shown an increased interest in such a single access point for controlled vocabularies. The paper outlines BARTOC's technical implementation, system architecture, and services in the light of semantic technologies. Its central thesis is that if the KOS community agreed on BARTOC as one of their main terminology registries, all involved parties would benefit from linked open knowledge organization systems.

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves., 34 pages, 2 tables, 2 figures

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language text. Knowledge in the text is modeled using a Neo Davidsonian-like formalism, represented as an answer set program. Relevant common sense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model common-sense reasoning methods required to accomplish these tasks. In this paper we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.

The concept1 of country has been widely used in all kinds of databases or systems. A commonly understood and unified schema for country entity is needed to knowledge organization, semantic association and deeply integration of the multi-source and heterogeneous data resources about country concepts. This paper proposes a computer understandable and computable Upper Country Ontology based on OWL and SKOS, with instances of 195 countries, 7 continents, 38 regions and more than 10 international organizations. The proposed ontology is expected to be used as knowledge middleware, supports identifying and indexing country entities, publishing open linked dataset, referencing country instance, SPARQL endpoint query and semantic reasoning and so on.

In the era of blended learning, integrated supplementary teaching materials are becoming more and more popular. However, the integrated supplementary teaching materials lack the navigation tools of the knowledge organization structure, and can not provide the learners with their learning trajectory. First of all, in this study, we designed the construction model of integrated supplementary materials; then, we constructed the framework of learning map, and designed the detailed contents of learning map nodes from three aspects: learning journey, knowledge point journey, and corresponding learning resources; finally, taking the integrated supplementary teaching materials of the one lesson one exercise in mathematics as an example, we collected the students' learning data and visualized the framework of learning map and the detailed contents of learning map nodes.

Traditional knowledge organization systems (KOS) including thesauri, classification schemes, taxonomies, subject heading systems, name authorities, and other lists of terms and codes have been playing important roles in indexing, information organization, and retrieval. With the advent of the semantic web, a large number of them have been converted into Linked Open Data (LOD) datasets. Since the Simple Knowledge Organization Systems (SKOS) and SKOS eXtension for Labels (SKOS-XL) are languages for representation of knowledge organization systems, they have been applied to knowledge organization systems. In this article, the issues surrounding changes, versioning control, and evolution of KOS are investigated. From KOS services providers and consumers perspectives, this study focuses on representation of changes on the semantic web.

The e-learning is a recent development that has emerged in the educational system due to the growth of the information technology. The common challenges involved in The e-learning platform include the collection and annotation of the learning materials, organization of the knowledge in a useful way, the retrieval and discovery of the useful learning materials from the knowledge space in a more significant way, and the delivery of the adaptive and personalized learning materials. In order to handle these challenges, the proposed system is developed using five different steps of knowledge input such as the annotation of the learning materials, creation of knowledge space, indexing of learning materials using the multi-dimensional knowledge and XML structure to generate a knowledge grid and the retrieval of learning materials performed by matching the user query with the indexed database and ontology. The process is carried out in two modules such as the server module and client module. The proposed approach is evaluated using various parameters such as the precision, recall and F-measure. Comprehensive results are achieved by varying the keywords, number of documents and the K-size. The proposed approach has yielded excellent results by obtaining the higher evaluation metric, together with an average precision of 0.81, average recall of 1 and average F-measure of 0.86 for K = 2.

Different kinds of technological data are available in manufacturing enterprises, concerning the resources available as well as the processes and the components needed for the production of specific products. These data usually are not stored in a centralized knowledge management system, thus one of the main problem of managers, especially in small enterprises, is to efficiently manage their data and reuse the knowledge deriving from previous products when a new product has to be produced. Starting form the analysis of the technological data available in manufacturing enterprises, we defined a formal model as set of matrices; their analysis allows the definition of a data model to structure the technological information. The model is at the basis of the proposed system, called manufacturing knowledge organization (MAKO) to support managers in structuring and reusing the technological knowledge available in their enterprise. A prototype of the MAKO system was implemented by using open-source software and its potentialities are shown in a case study.

There is a wealth of conservation-related resources that are published online on institutional and personal websites. There is value in searching across these websites, but this is currently impossible because the published data do not conform to any universal standard. This paper begins with a review of the types of classifications employed for conservation content in several conservation websites. It continues with an analysis of these classifications and it identifies some of their limitations that are related to the lack of conceptual basis of the classification terms used. The paper then draws parallels with similar problems in other professional fields and investigates the technologies used to resolve them. Solutions developed in the fields of computer science and knowledge organization are then described. The paper continues with the survey of two important resources in cultural heritage: the ICOM-CIDOC-CRM and the Getty vocabularies and it explains how these resources can be combined in the field ...