Search (42 results, page 1 of 3)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Wissensrepräsentation"
  1. Rocha Souza, R.; Lemos, D.: a comparative analysis : Knowledge organization systems for the representation of multimedia resources on the Web (2020) 0.11
    0.10573533 = product of:
      0.14098044 = sum of:
        0.01029941 = weight(_text_:information in 5993) [ClassicSimilarity], result of:
          0.01029941 = score(doc=5993,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.116372846 = fieldWeight in 5993, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=5993)
        0.093888626 = weight(_text_:standards in 5993) [ClassicSimilarity], result of:
          0.093888626 = score(doc=5993,freq=4.0), product of:
            0.22470023 = queryWeight, product of:
              4.4569545 = idf(docFreq=1393, maxDocs=44218)
              0.050415643 = queryNorm
            0.41783947 = fieldWeight in 5993, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4569545 = idf(docFreq=1393, maxDocs=44218)
              0.046875 = fieldNorm(doc=5993)
        0.036792405 = product of:
          0.07358481 = sum of:
            0.07358481 = weight(_text_:organization in 5993) [ClassicSimilarity], result of:
              0.07358481 = score(doc=5993,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.40937364 = fieldWeight in 5993, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5993)
          0.5 = coord(1/2)
      0.75 = coord(3/4)
    
    Abstract
    The lack of standardization in the production, organization and dissemination of information in documentation centers and institutions alike, as a result from the digitization of collections and their availability on the internet has called for integration efforts. The sheer availability of multimedia content has fostered the development of many distinct and, most of the time, independent metadata standards for its description. This study aims at presenting and comparing the existing standards of metadata, vocabularies and ontologies for multimedia annotation and also tries to offer a synthetic overview of its main strengths and weaknesses, aiding efforts for semantic integration and enhancing the findability of available multimedia resources on the web. We also aim at unveiling the characteristics that could, should and are perhaps not being highlighted in the characterization of multimedia resources.
    Source
    Knowledge organization. 47(2020) no.4, S.300-319
  2. MacFarlane, A.; Missaoui, S.; Frankowska-Takhari, S.: On machine learning and knowledge organization in multimedia information retrieval (2020) 0.03
    0.0336169 = product of:
      0.0672338 = sum of:
        0.017165681 = weight(_text_:information in 5732) [ClassicSimilarity], result of:
          0.017165681 = score(doc=5732,freq=8.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.19395474 = fieldWeight in 5732, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5732)
        0.050068118 = product of:
          0.100136235 = sum of:
            0.100136235 = weight(_text_:organization in 5732) [ClassicSimilarity], result of:
              0.100136235 = score(doc=5732,freq=16.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.55708694 = fieldWeight in 5732, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5732)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).
    Source
    Knowledge organization. 47(2020) no.1, S.45-55
  3. Broughton, V.: Science and knowledge organization : an editorial (2021) 0.03
    0.02597155 = product of:
      0.0519431 = sum of:
        0.008582841 = weight(_text_:information in 593) [ClassicSimilarity], result of:
          0.008582841 = score(doc=593,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 593, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=593)
        0.04336026 = product of:
          0.08672052 = sum of:
            0.08672052 = weight(_text_:organization in 593) [ClassicSimilarity], result of:
              0.08672052 = score(doc=593,freq=12.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.48245144 = fieldWeight in 593, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=593)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.
    Footnote
    Editorial zu einem Special issue on 'Science and knowledge organization' mit längeren Überblicken zu wichtigen Begriffen der Wissensorgansiation.
    Source
    Knowledge organization. 48(2021) no.7/8, S.469-472
  4. Amirhosseini, M.; Avidan, G.: ¬A dialectic perspective on the evolution of thesauri and ontologies (2021) 0.02
    0.021993173 = product of:
      0.043986347 = sum of:
        0.008582841 = weight(_text_:information in 592) [ClassicSimilarity], result of:
          0.008582841 = score(doc=592,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 592, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=592)
        0.035403505 = product of:
          0.07080701 = sum of:
            0.07080701 = weight(_text_:organization in 592) [ClassicSimilarity], result of:
              0.07080701 = score(doc=592,freq=8.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.39391994 = fieldWeight in 592, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=592)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.
    Source
    Knowledge organization. 48(2021) no.6, S.403-429
  5. Fagundes, P.B.; Freund, G.P.; Vital, L.P.; Monteiro de Barros, C.; Macedo, D.D.J.de: Taxonomias, ontologias e tesauros : possibilidades de contribuição para o processo de Engenharia de Requisitos (2020) 0.02
    0.02109987 = product of:
      0.04219974 = sum of:
        0.017165681 = weight(_text_:information in 5828) [ClassicSimilarity], result of:
          0.017165681 = score(doc=5828,freq=8.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.19395474 = fieldWeight in 5828, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5828)
        0.025034059 = product of:
          0.050068118 = sum of:
            0.050068118 = weight(_text_:organization in 5828) [ClassicSimilarity], result of:
              0.050068118 = score(doc=5828,freq=4.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.27854347 = fieldWeight in 5828, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5828)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Some of the fundamental activities of the software development process are related to the discipline of Requirements Engineering, whose objective is the discovery, analysis, documentation and verification of the requirements that will be part of the system. Requirements are the conditions or capabilities that software must have or perform to meet the users needs. The present study is being developed to propose a model of cooperation between Information Science and Requirements Engineering. Aims to present the analysis results on the possibilities of using the knowledge organization systems: taxonomies, thesauri and ontologies during the activities of Requirements Engineering: design, survey, elaboration, negotiation, specification, validation and requirements management. From the results obtained it was possible to identify in which stage of the Requirements Engineering process, each type of knowledge organization system could be used. We expect that this study put in evidence the need for new researchs and proposals to strengt the exchange between Information Science, as a science that has information as object of study, and the Requirements Engineering which has in the information the raw material to identify the informational needs of software users.
  6. Si, L.; Zhou, J.: Ontology and linked data of Chinese great sites information resources from users' perspective (2022) 0.02
    0.019621588 = product of:
      0.039243177 = sum of:
        0.008582841 = weight(_text_:information in 1115) [ClassicSimilarity], result of:
          0.008582841 = score(doc=1115,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 1115, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1115)
        0.030660335 = product of:
          0.06132067 = sum of:
            0.06132067 = weight(_text_:organization in 1115) [ClassicSimilarity], result of:
              0.06132067 = score(doc=1115,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.34114468 = fieldWeight in 1115, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1115)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Great Sites are closely related to the residents' life, urban and rural development. In the process of rapid urbanization in China, the protection and utilization of Great Sites are facing unprecedented pressure. Effective knowl­edge organization with ontology and linked data of Great Sites is a prerequisite for their protection and utilization. In this paper, an interview is conducted to understand the users' awareness towards Great Sites to build the user-centered ontology. As for designing the Great Site ontology, firstly, the scope of Great Sites is determined. Secondly, CIDOC- CRM and OWL-Time Ontology are reused combining the results of literature research and user interviews. Thirdly, the top-level structure and the specific instances are determined to extract knowl­edge concepts of Great Sites. Fourthly, they are transformed into classes, data properties and object properties of the Great Site ontology. Later, based on the linked data technology, taking the Great Sites in Xi'an Area as an example, this paper uses D2RQ to publish the linked data set of the knowl­edge of the Great Sites and realize its opening and sharing. Semantic services such as semantic annotation, semantic retrieval and reasoning are provided based on the ontology.
    Content
    Vgl.: https://www.nomos-elibrary.de/10.5771/0943-7444-2022-8/ko-knowledge-organization-jahrgang-49-2022-heft-8.
    Source
    Knowledge organization. 49(2022) no.8, S.547 - 562
  7. Ghosh, S.S.; Das, S.; Chatterjee, S.K.: Human-centric faceted approach for ontology construction (2020) 0.02
    0.017433718 = product of:
      0.034867436 = sum of:
        0.017165681 = weight(_text_:information in 5731) [ClassicSimilarity], result of:
          0.017165681 = score(doc=5731,freq=8.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.19395474 = fieldWeight in 5731, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5731)
        0.017701752 = product of:
          0.035403505 = sum of:
            0.035403505 = weight(_text_:organization in 5731) [ClassicSimilarity], result of:
              0.035403505 = score(doc=5731,freq=2.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.19695997 = fieldWeight in 5731, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5731)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    In this paper, we propose an ontology building method, called human-centric faceted approach for ontology construction (HCFOC). HCFOC uses the human-centric approach, improvised with the idea of selective dissemination of information (SDI), to deal with context. Further, this ontology construction process makes use of facet analysis and an analytico-synthetic classification approach. This novel fusion contributes to the originality of HCFOC and distinguishes it from other existing ontology construction methodologies. Based on HCFOC, an ontology of the tourism domain has been designed using the Protégé-5.5.0 ontology editor. The HCFOC methodology has provided the necessary flexibility, extensibility, robustness and has facilitated the capturing of background knowledge. It models the tourism ontology in such a way that it is able to deal with the context of a tourist's information need with precision. This is evident from the result that more than 90% of the user's queries were successfully met. The use of domain knowledge and techniques from both library and information science and computer science has helped in the realization of the desired purpose of this ontology construction process. It is envisaged that HCFOC will have implications for ontology developers. The demonstrated tourism ontology can support any tourism information retrieval system.
    Source
    Knowledge organization. 47(2020) no.1, S.31-44
  8. Campos, L.M.: Princípios teóricos usados na elaboracao de ontologias e sua influência na recuperacao da informacao com uso de de inferências [Theoretical principles used in ontology building and their influence on information retrieval using inferences] (2021) 0.02
    0.016283836 = product of:
      0.032567672 = sum of:
        0.014865918 = weight(_text_:information in 826) [ClassicSimilarity], result of:
          0.014865918 = score(doc=826,freq=6.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.16796975 = fieldWeight in 826, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=826)
        0.017701752 = product of:
          0.035403505 = sum of:
            0.035403505 = weight(_text_:organization in 826) [ClassicSimilarity], result of:
              0.035403505 = score(doc=826,freq=2.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.19695997 = fieldWeight in 826, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=826)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Several instruments of knowledge organization will reflect different possibilities for information retrieval. In this context, ontologies have a different potential because they allow knowledge discovery, which can be used to retrieve information in a more flexible way. However, this potential can be affected by the theoretical principles adopted in ontology building. The aim of this paper is to discuss, in an introductory way, how a (not exhaustive) set of theoretical principles can influence an aspect of ontologies: their use to obtain inferences. In this context, the role of Ingetraut Dahlberg's Theory of Concept is discussed. The methodology is exploratory, qualitative, and from the technical point of view it uses bibliographic research supported by the content analysis method. It also presents a small example of application as a proof of concept. As results, a discussion about the influence of conceptual definition on subsumption inferences is presented, theoretical contributions are suggested that should be used to guide the formation of hierarchical structures on which such inferences are supported, and examples are provided of how the absence of such contributions can lead to erroneous inferences
  9. Banerjee, D.; Ghosh, S.S.; Mondal, T.M.: OnE : an ontology evaluation framework (2020) 0.02
    0.015770756 = product of:
      0.03154151 = sum of:
        0.01029941 = weight(_text_:information in 5898) [ClassicSimilarity], result of:
          0.01029941 = score(doc=5898,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.116372846 = fieldWeight in 5898, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=5898)
        0.021242103 = product of:
          0.042484205 = sum of:
            0.042484205 = weight(_text_:organization in 5898) [ClassicSimilarity], result of:
              0.042484205 = score(doc=5898,freq=2.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.23635197 = fieldWeight in 5898, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5898)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    A comprehensive set of evaluation criteria, named OnE, for evaluating ontologies has been proposed in this paper. Each criterion of OnE has been defined in a way such that together they are capable of evaluating any ontology from all aspects. The process of using OnE for evaluation has been demonstrated by evaluating chemical ontologies. Also, for this purpose, an ontology on the domain of agricultural chemicals has been constructed by following the human-centric faceted approach for ontology construction (HCFOC) and has been evaluated using OnE. The results obtained after the evaluation has provided insights about the ontologies. The constructed ontology aims to support any information system trying to support farmers in the process of decision making while selecting chemicals for use in agriculture. Also, it is envisaged that the demonstrated ontology and the set of evaluation criteria named OnE will redefine ontology evaluation and make it easy while making a strong impact on ontology developers.
    Source
    Knowledge organization. 47(2020) no.4, S.283-299
  10. Balakrishnan, U,; Soergel, D.; Helfer, O.: Representing concepts through description logic expressions for knowledge organization system (KOS) mapping (2020) 0.02
    0.0153301675 = product of:
      0.06132067 = sum of:
        0.06132067 = product of:
          0.12264134 = sum of:
            0.12264134 = weight(_text_:organization in 144) [ClassicSimilarity], result of:
              0.12264134 = score(doc=144,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.68228936 = fieldWeight in 144, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.078125 = fieldNorm(doc=144)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Series
    Advances in knowledge organization; vol.17
    Source
    Knowledge Organization at the Interface. Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark. Ed.: M. Lykke et al
  11. Oliveira Machado, L.M.; Almeida, M.B.; Souza, R.R.: What researchers are currently saying about ontologies : a review of recent Web of Science articles (2020) 0.01
    0.014919861 = product of:
      0.029839722 = sum of:
        0.01213797 = weight(_text_:information in 5881) [ClassicSimilarity], result of:
          0.01213797 = score(doc=5881,freq=4.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.13714671 = fieldWeight in 5881, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5881)
        0.017701752 = product of:
          0.035403505 = sum of:
            0.035403505 = weight(_text_:organization in 5881) [ClassicSimilarity], result of:
              0.035403505 = score(doc=5881,freq=2.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.19695997 = fieldWeight in 5881, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5881)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Traditionally connected to philosophy, the term ontology is increasingly related to information systems areas. Some researchers consider the approaches of the two disciplinary contexts to be completely different. Others consider that, although different, they should talk to each other, as both seek to answer similar questions. With the extensive literature on this topic, we intend to contribute to the understanding of the use of the term ontology in current research and which references support this use. An exploratory study was developed with a mixed methodology and a sample collected from the Web of Science of articles publishe in 2018. The results show the current prevalence of computer science in studies related to ontology and also of Gruber's view suggesting ontology as kind of conceptualization, a dominant view in that field. Some researchers, particularly in the field of biomedicine, do not adhere to this dominant view but to another one that seems closer to ontological study in the philosophical context. The term ontology, in the context of information systems, appears to be consolidating with a meaning different from the original, presenting traces of the process of "metaphorization" in the transfer of the term between the two fields of study.
    Source
    Knowledge organization. 47(2020) no.3, S.199-219
  12. Hudon, M.: Facet (2020) 0.01
    0.0138538135 = product of:
      0.055415254 = sum of:
        0.055415254 = product of:
          0.11083051 = sum of:
            0.11083051 = weight(_text_:organization in 5899) [ClassicSimilarity], result of:
              0.11083051 = score(doc=5899,freq=10.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.6165823 = fieldWeight in 5899, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=5899)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    S.R. Ranganathan is credited with the introduction of the term "facet" in the field of knowledge organization towards the middle of the twentieth century. Facets have traditionally been used to organize document collections and to express complex subjects. In the digital world, they act as filters to facilitate navigation and improve retrieval. But the popularity of the term does not mean that a definitive characterization of the concept has been established. Indeed, several conceptualizations of the facet co-exist. This article provides an overview of formal and informal definitions found in the literature of knowledge organization, followed by a discussion of four common conceptualizations of the facet: process vs product, nature vs function, object vs subject and organization vs navigation.
    Series
    Reviews of concepts in knowledge organization
    Source
    Knowledge organization. 47(2020) no.4, S.320-333
  13. Kleineberg, M.: Classifying perspectives : expressing levels of knowing in the Integrative Levels Classification (2020) 0.01
    0.012517029 = product of:
      0.050068118 = sum of:
        0.050068118 = product of:
          0.100136235 = sum of:
            0.100136235 = weight(_text_:organization in 81) [ClassicSimilarity], result of:
              0.100136235 = score(doc=81,freq=4.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.55708694 = fieldWeight in 81, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.078125 = fieldNorm(doc=81)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Series
    Advances in knowledge organization; vol.17
    Source
    Knowledge Organization at the Interface. Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark. Ed.: M. Lykke et al
  14. Biagetti, M.T.: Ontologies as knowledge organization systems (2021) 0.01
    0.012391226 = product of:
      0.049564905 = sum of:
        0.049564905 = product of:
          0.09912981 = sum of:
            0.09912981 = weight(_text_:organization in 439) [ClassicSimilarity], result of:
              0.09912981 = score(doc=439,freq=8.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.5514879 = fieldWeight in 439, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=439)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    This contribution presents the principal features of ontologies, drawing special attention to the comparison between ontologies and the different kinds of know­ledge organization systems (KOS). The focus is on the semantic richness exhibited by ontologies, which allows the creation of a great number of relationships between terms. That establishes ontologies as the most evolved type of KOS. The concepts of "conceptualization" and "formalization" and the key components of ontologies are described and discussed, along with upper and domain ontologies and special typologies, such as bibliographical ontologies and biomedical ontologies. The use of ontologies in the digital libraries environment, where they have replaced thesauri for query expansion in searching, and the role they are playing in the Semantic Web, especially for semantic interoperability, are sketched.
    Series
    Reviews of Concepts in Knowledge Organization
    Source
    Knowledge organization. 48(2021) no.2, S.152-176
  15. Hocker, J.; Schindler, C.; Rittberger, M.: Participatory design for ontologies : a case study of an open science ontology for qualitative coding schemas (2020) 0.01
    0.011685811 = product of:
      0.023371622 = sum of:
        0.009710376 = weight(_text_:information in 179) [ClassicSimilarity], result of:
          0.009710376 = score(doc=179,freq=4.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.10971737 = fieldWeight in 179, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=179)
        0.013661247 = product of:
          0.027322493 = sum of:
            0.027322493 = weight(_text_:22 in 179) [ClassicSimilarity], result of:
              0.027322493 = score(doc=179,freq=2.0), product of:
                0.17654699 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050415643 = queryNorm
                0.15476047 = fieldWeight in 179, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03125 = fieldNorm(doc=179)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Date
    20. 1.2015 18:30:22
    Footnote
    Beitrag in einem Special Issue: Showcasing Doctoral Research in Information Science.
    Source
    Aslib journal of information management. 72(2020) no.4, S.671-685
  16. Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo (2020) 0.01
    0.009681771 = product of:
      0.038727082 = sum of:
        0.038727082 = weight(_text_:standards in 53) [ClassicSimilarity], result of:
          0.038727082 = score(doc=53,freq=2.0), product of:
            0.22470023 = queryWeight, product of:
              4.4569545 = idf(docFreq=1393, maxDocs=44218)
              0.050415643 = queryNorm
            0.17234999 = fieldWeight in 53, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4569545 = idf(docFreq=1393, maxDocs=44218)
              0.02734375 = fieldNorm(doc=53)
      0.25 = coord(1/4)
    
    Content
    # Community action on all ontologies (quality, FAIRness, conformity) Archivo is extensible and allows contributions to give consumers a central place to encode their requirements. We envision fostering adherence to standards and strengthening incentives for publishers to build a better (FAIRer) web of ontologies. 1. SHACL (https://www.w3.org/TR/shacl/, co-edited by DBpedia's CTO D. Kontokostas) enables easy testing of ontologies. Archivo offers free SHACL continuous integration testing for ontologies. Anyone can implement their SHACL tests and add them to the SHACL library on Github. We believe that there are many synergies, i.e. SHACL tests for your ontology are helpful for others as well. 2. We are looking for ontology experts to join DBpedia and discuss further validation (e.g. stars) to increase FAIRness and quality of ontologies. We are forming a steering committee and also a PC for the upcoming Vocarnival at SEMANTiCS 2021. Please message hellmann@informatik.uni-leipzig.de <mailto:hellmann@informatik.uni-leipzig.de>if you would like to join. We would like to extend the Archivo platform with relevant visualisations, tests, editing aides, mapping management tools and quality checks.
  17. Simoes, G.; Machado, L.; Gnoli, C.; Souza, R.: Can an ontologically-oriented KO do without concepts? (2020) 0.01
    0.009198101 = product of:
      0.036792405 = sum of:
        0.036792405 = product of:
          0.07358481 = sum of:
            0.07358481 = weight(_text_:organization in 4964) [ClassicSimilarity], result of:
              0.07358481 = score(doc=4964,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.40937364 = fieldWeight in 4964, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4964)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    The ontological approach in the development of KOS is an attempt to overcome the limitations of the traditional epistemological approach. Questions raise about the representation and organization of ontologically-oriented KO units, such as BFO universals or ILC phenomena. The study aims to compare the ontological approaches of BFO and ILC using a hermeneutic approach. We found that the differences between the units of the two systems are primarily due to the formal level of abstraction of BFO and the different organizations, namely the grouping of phenomena into ILC classes that represent complex compounds of entities in the BFO approach. In both systems the use of concepts is considered instrumental, although in the ILC they constitute the intersubjective component of the phenomena whereas in BFO they serve to access the entities of reality but are not part of them.
    Series
    Advances in knowledge organization; vol.17
    Source
    Knowledge Organization at the Interface. Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark. Ed.: M. Lykke et al
  18. Machado, L.M.O.: Ontologies in knowledge organization (2021) 0.01
    0.009198101 = product of:
      0.036792405 = sum of:
        0.036792405 = product of:
          0.07358481 = sum of:
            0.07358481 = weight(_text_:organization in 198) [ClassicSimilarity], result of:
              0.07358481 = score(doc=198,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.40937364 = fieldWeight in 198, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.046875 = fieldNorm(doc=198)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    Within the knowledge organization systems (KOS) set, the term "ontology" is paradigmatic of the terminological ambiguity in different typologies. Contributing to this situation is the indiscriminate association of the term "ontology", both as a specific type of KOS and as a process of categorization, due to the interdisciplinary use of the term with different meanings. We present a systematization of the perspectives of different authors of ontologies, as representational artifacts, seeking to contribute to terminological clarification. Focusing the analysis on the intention, semantics and modulation of ontologies, it was possible to notice two broad perspectives regarding ontologies as artifacts that coexist in the knowledge organization systems spectrum. We have ontologies viewed, on the one hand, as an evolution in terms of complexity of traditional conceptual systems, and on the other hand, as a system that organizes ontological rather than epistemological knowledge. The focus of ontological analysis is the item to model and not the intentions that motivate the construction of the system.
  19. Buente, W.; Baybayan, C.K.; Hajibayova, L.; McCorkhill, M.; Panchyshyn, R.: Exploring the renaissance of wayfinding and voyaging through the lens of knowledge representation, organization and discovery systems (2020) 0.01
    0.008850876 = product of:
      0.035403505 = sum of:
        0.035403505 = product of:
          0.07080701 = sum of:
            0.07080701 = weight(_text_:organization in 105) [ClassicSimilarity], result of:
              0.07080701 = score(doc=105,freq=8.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.39391994 = fieldWeight in 105, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=105)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    The purpose of this paper is to provide a critical analysis from an ethical perspective of how the concept of indigenous wayfinding and voyaging is mapped in knowledge representation, organization and discovery systems. Design/methodology/approach In this study, the Dewey Decimal Classification, the Library of Congress Subject Headings, the Library of Congress Classifications systems and the Web of Science citation database were methodically examined to determine how these systems represent and facilitate the discovery of indigenous knowledge of wayfinding and voyaging. Findings The analysis revealed that there was no dedicated representation of the indigenous practices of wayfinding and voyaging in the major knowledge representation, organization and discovery systems. By scattering indigenous practice across various, often very broad and unrelated classes, coherence in the record is disrupted, resulting in misrepresentation of these indigenous concepts. Originality/value This study contributes to a relatively limited research literature on representation and organization of indigenous knowledge of wayfinding and voyaging. This study calls to foster a better understanding and appreciation for the rich knowledge that indigenous cultures provide for an enlightened society.
  20. Peponakis, M.; Mastora, A.; Kapidakis, S.; Doerr, M.: Expressiveness and machine processability of Knowledge Organization Systems (KOS) : an analysis of concepts and relations (2020) 0.01
    0.0076650837 = product of:
      0.030660335 = sum of:
        0.030660335 = product of:
          0.06132067 = sum of:
            0.06132067 = weight(_text_:organization in 5787) [ClassicSimilarity], result of:
              0.06132067 = score(doc=5787,freq=6.0), product of:
                0.17974974 = queryWeight, product of:
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.050415643 = queryNorm
                0.34114468 = fieldWeight in 5787, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.5653565 = idf(docFreq=3399, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5787)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    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.

Authors

Languages

  • e 37
  • pt 3
  • d 1
  • More… Less…

Types

  • a 37
  • el 8
  • p 3
  • A 1
  • EL 1
  • More… Less…