Search (72 results, page 2 of 4)

  • × theme_ss:"Wissensrepräsentation"
  • × year_i:[2010 TO 2020}
  1. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.01 
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    Date
    23. 7.2017 13:49:22
  2. Prud'hommeaux, E.; Gayo, E.: RDF ventures to boldly meet your most pedestrian needs (2015) 0.01 
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    Source
    Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22
  3. Menzel, C.: Knowledge representation, the World Wide Web, and the evolution of logic (2011) 0.01 
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  4. Amarger, F.; Chanet, J.-P.; Haemmerlé, O.; Hernandez, N.; Roussey, C.: SKOS sources transformations for ontology engineering : agronomical taxonomy use case (2014) 0.01 
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  5. Rosemblat, G.; Resnick, M.P.; Auston, I.; Shin, D.; Sneiderman, C.; Fizsman, M.; Rindflesch, T.C.: Extending SemRep to the public health domain (2013) 0.01 
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  6. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.01 
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  7. Xu, Y.; Li, G.; Mou, L.; Lu, Y.: Learning non-taxonomic relations on demand for ontology extension (2014) 0.01 
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    Abstract
    Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.
  8. Ma, X.; Carranza, E.J.M.; Wu, C.; Meer, F.D. van der; Liu, G.: ¬A SKOS-based multilingual thesaurus of geological time scale for interoperability of online geological maps (2011) 0.01 
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    Content
    Article Outline 1. Introduction 2. SKOS-based multilingual thesaurus of geological time scale 2.1. Addressing the insufficiency of SKOS in the context of the Semantic Web 2.2. Addressing semantics and syntax/lexicon in multilingual GTS terms 2.3. Extending SKOS model to capture GTS structure 2.4. Summary of building the SKOS-based MLTGTS 3. Recognizing and translating GTS terms retrieved from WMS 4. Pilot system, results, and evaluation 5. Discussion 6. Conclusions Vgl. unter: http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271720&_user=3865853&_pii=S0098300411000744&_check=y&_origin=&_coverDate=31-Oct-2011&view=c&wchp=dGLbVlt-zSkzS&_valck=1&md5=e2c1daf53df72d034d22278212578f42&ie=/sdarticle.pdf.
  9. Kless, D.: Erstellung eines allgemeinen Standards zur Wissensorganisation : Nutzen, Möglichkeiten, Herausforderungen, Wege (2010) 0.01 
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  10. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.01 
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    Date
    22. 2.2013 11:39:23
  11. Mahesh, K.: Highly expressive tagging for knowledge organization in the 21st century (2014) 0.01 
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    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
  12. Kiren, T.; Shoaib, M.: ¬A novel ontology matching approach using key concepts (2016) 0.01 
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    Date
    20. 1.2015 18:30:22
  13. Conde, A.; Larrañaga, M.; Arruarte, A.; Elorriaga, J.A.; Roth, D.: litewi: a combined term extraction and entity linking method for eliciting educational ontologies from textbooks (2016) 0.01 
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    Date
    22. 1.2016 12:38:14
  14. Marcondes, C.H.; Costa, L.C da.: ¬A model to represent and process scientific knowledge in biomedical articles with semantic Web technologies (2016) 0.01 
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    Date
    12. 3.2016 13:17:22
  15. Das, S.; Roy, S.: Faceted ontological model for brain tumour study (2016) 0.01 
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    12. 3.2016 13:21:22
  16. Bringsjord, S.; Clark, M.; Taylor, J.: Sophisticated knowledge representation and reasoning requires philosophy (2014) 0.01 
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    Date
    9. 2.2017 19:22:14
  17. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.01 
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    Date
    16.11.2018 14:22:01
  18. Gnoli, C.; Pullman, T.; Cousson, P.; Merli, G.; Szostak, R.: Representing the structural elements of a freely faceted classification (2011) 0.00 
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  19. Lassalle, E.; Lassalle, E.: Semantic models in information retrieval (2012) 0.00 
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    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  20. Djioua, B.; Desclés, J.-P.; Alrahabi, M.: Searching and mining with semantic categories (2012) 0.00 
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    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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