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  1. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.05 
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    Classification
    BCA (FH K)
    Date
    23. 7.2017 13:49:22
    GHBS
    BCA (FH K)
  2. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.05 
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  3. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.04 
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    Date
    1. 8.2010 12:35:22
  4. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.04 
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    Source
    Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
  5. Mahesh, K.: Highly expressive tagging for knowledge organization in the 21st century (2014) 0.03 
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    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  6. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.03 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  7. Hitzler, P.; Janowicz, K.: Ontologies in a data driven world : finding the middle ground (2013) 0.02 
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  8. Kottmann, N.; Studer, T.: Improving semantic query answering (2006) 0.02 
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    Abstract
    The retrieval problem is one of the main reasoning tasks for knowledge base systems. Given a knowledge base K and a concept C, the retrieval problem consists of finding all individuals a for which K logically entails C(a). We present an approach to answer retrieval queries over (a restriction of) OWL ontologies. Our solution is based on reducing the retrieval problem to a problem of evaluating an SQL query over a database constructed from the original knowledge base. We provide complete answers to retrieval problems. Still, our system performs very well as is shown by a standard benchmark.
  9. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.02 
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    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  10. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.02 
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  11. Stuckenschmidt, H.: Ontologien : Konzepte, Technologien und Anwendungen (2009) 0.02 
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    Classification
    TWX (FH K)
    GHBS
    TWX (FH K)
  12. Loehrlein, A.; Jacob, E.K.; Lee, S.; Yang, K.: Development of heuristics in a hybrid approach to faceted classification (2006) 0.02 
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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
  13. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.02 
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    Pages
    S.11-22
  14. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02 
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    Date
    26.12.2011 13:22:07
  15. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.01 
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    Abstract
    On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.
  16. Nguyen, P.H.P.; Kaneiwa, K.; Nguyen, M.-Q.: Ontology inferencing rules and operations in conceptual structure theory (2010) 0.01 
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    Source
    Advances in ontologies: Proceedings of the Sixth Australasian Ontology Workshop Adelaide, Australia, 7 December 2010. Eds.: K. Taylor, T.Meyer u. M.Orgun [http://krr.meraka.org.za/~aow2010/AOW2010-preproceedings.pdf]
  17. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.01 
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    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  18. Thellefsen, M.: ¬The dynamics of information representation and knowledge mediation (2006) 0.01 
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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
  19. Mainz, I.; Weller, K.; Paulsen, I.; Mainz, D.; Kohl, J.; Haeseler, A. von: Ontoverse : collaborative ontology engineering for the life sciences (2008) 0.01 
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  20. Falkenberg, E.; Hesse, W.; Lindgreen, P.; Nilsson, B.E.; Oei, J.L.H.; Rolland, C.; Stamper, R.K.; Van Assche, F.J.M.; Verrijn-Stuart, A.A.; Voss, K.: FRISCO - A framework of information system concepts : the FRISCO report; final draft (1996) 0.01 
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