Search (304 results, page 1 of 16)

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  • × theme_ss:"Wissensrepräsentation"
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  1. 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) 0.06
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
    Pages
    S.140-150
  2. 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
    Pages
    S.203-214
  3. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.04
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    Pages
    S.235-243
    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
  4. Mahesh, K.: Highly expressive tagging for knowledge organization in the 21st century (2014) 0.03
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    Abstract
    Knowledge organization of large-scale content on the Web requires substantial amounts of semantic metadata that is expensive to generate manually. Recent developments in Web technologies have enabled any user to tag documents and other forms of content thereby generating metadata that could help organize knowledge. However, merely adding one or more tags to a document is highly inadequate to capture the aboutness of the document and thereby to support powerful semantic functions such as automatic classification, question answering or true semantic search and retrieval. This is true even when the tags used are labels from a well-designed classification system such as a thesaurus or taxonomy. There is a strong need to develop a semantic tagging mechanism with sufficient expressive power to capture the aboutness of each part of a document or dataset or multimedia content in order to enable applications that can benefit from knowledge organization on the Web. This article proposes a highly expressive mechanism of using ontology snippets as semantic tags that map portions of a document or a part of a dataset or a segment of a multimedia content to concepts and relations in an ontology of the domain(s) of interest.
    Pages
    S.274-280
    Source
    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
  5. 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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    Pages
    S.255-261
    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
  6. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.02
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    Pages
    S.11-22
  7. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.02
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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.
    Source
    Journal of the Association for Information Science and Technology. 75(2023) no.2, S.167-187
  8. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.02
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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.
    Source
    Information processing and management. 50(2014) no.1, S.132-155
  9. Nguyen, P.H.P.; Kaneiwa, K.; Nguyen, M.-Q.: Ontology inferencing rules and operations in conceptual structure theory (2010) 0.02
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    Pages
    S.61-70
    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]
  10. Thellefsen, M.: ¬The dynamics of information representation and knowledge mediation (2006) 0.02
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    Pages
    S.279-286
    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
  11. Mainz, I.; Weller, K.; Paulsen, I.; Mainz, D.; Kohl, J.; Haeseler, A. von: Ontoverse : collaborative ontology engineering for the life sciences (2008) 0.02
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    Source
    Information - Wissenschaft und Praxis. 59(2008) H.2, S.91-99
  12. Weller, K.; Peters, I.: Reconsidering relationships for knowledge representation (2007) 0.02
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    Pages
    S.501-504
  13. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.02
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    Abstract
    Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.
    Date
    22. 1.2016 17:30:31

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