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  1. Frâncu, V.: Subjects in FRBR and poly-hierarchical thesauri as possible knowledge organization tools (2006) 0.00 
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    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  2. Jiang, X.; Tan, A.-H.: CRCTOL: a semantic-based domain ontology learning system (2009) 0.00 
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    Date
    4. 2.2010 20:54:54
  3. Sanatjoo, A.: Development of thesaurus structure through a work-task oriented methodology 0.00 
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    Date
    4. 6.2010 17:59:19
  4. Crystal, D.: Semantic targeting : past, present, and future (2010) 0.00 
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    Source
    Aslib proceedings. 62(2010) nos.4/5, S.355-365
  5. Gray, A.J.G.; Gray, N.; Hall, C.W.; Ounis, I.: Finding the right term : retrieving and exploring semantic concepts in astronomical vocabularies (2010) 0.00 
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    Source
    Information processing and management. 46(2010) no.4, S.470-478
  6. Yu, L.-C.; Wu, C.-H.; Chang, R.-Y.; Liu, C.-H.; Hovy, E.H.: Annotation and verification of sense pools in OntoNotes (2010) 0.00 
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    Information processing and management. 46(2010) no.4, S.436-447
  7. Aker, A.; Plaza, L.; Lloret, E.; Gaizauskas, R.: Do humans have conceptual models about geographic objects? : a user study (2013) 0.00 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.689-700
  8. Bast, H.; Bäurle, F.; Buchhold, B.; Haussmann, E.: Broccoli: semantic full-text search at your fingertips (2012) 0.00 
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    Date
    4. 4.2013 20:58:03
  9. Giunchiglia, F.; Dutta, B.; Maltese, V.: From knowledge organization to knowledge representation (2014) 0.00 
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    Date
    4. 9.2014 13:13:36
  10. Pieterse, V.; Kourie, D.G.: Lists, taxonomies, lattices, thesauri and ontologies : paving a pathway through a terminological jungle (2014) 0.00 
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    4. 9.2014 13:13:56
  11. Aparecida Moura, M.: Emerging discursive formations, folksonomy and social semantic information spaces (SSIS) : the contributions of the theory of integrative levels in the studies carried out by the Classification Research Group (CRG) (2014) 0.00 
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    Source
    Knowledge organization. 41(2014) no.4, S.304-310
  12. Netto, C.M.; Borém de Oliveira Lima, G.A.; Pierozzi Júnior, I.: ¬An application of facet analysis theory and concept maps for faceted search in a domain ontology : preliminary studies (2016) 0.00 
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    Knowledge organization. 43(2016) no.4, S.254-264
  13. Frické, M.: Logical division (2016) 0.00 
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    Content
    Contents: 1. Introduction: Kinds of Division; 2. The Basics of Logical Division; 3. History; 4. Formalization; 5. The Rules; 6. The Status of the Rules; 7. The Process of Logical Division; 8. Conclusion
  14. MacFarlane, A.; Missaoui, S.; Frankowska-Takhari, S.: On machine learning and knowledge organization in multimedia information retrieval (2020) 0.00 
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    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).
  15. Sinha, P.K.; Dutta, B.: ¬A systematic analysis of flood ontologies : a parametric approach (2020) 0.00 
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    Date
    4. 3.2020 14:57:06
  16. Xu, G.; Cao, Y.; Ren, Y.; Li, X.; Feng, Z.: Network security situation awareness based on semantic ontology and user-defined rules for Internet of Things (2017) 0.00 
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    Date
    4. 9.2017 10:43:54
  17. Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014) 0.00 
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    Date
    20. 1.2015 18:30:22
  18. Fernández, M.; Cantador, I.; López, V.; Vallet, D.; Castells, P.; Motta, E.: Semantically enhanced Information Retrieval : an ontology-based approach (2011) 0.00 
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    Source
    Web semantics: science, services and agents on the World Wide Web. 9(2011) no.4, S.434-452
  19. Boteram, F.: "Content architecture" : semantic interoperability in an international comprehensive knowledge organisation system (2010) 0.00 
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    Aslib proceedings. 62(2010) nos.4/5, S.406-414
  20. Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014) 0.00 
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    Date
    20. 1.2015 18:30:22

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