Search (51 results, page 1 of 3)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Wissensrepräsentation"
  1. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.03 
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    Abstract
    Considering the characteristics of hypertext systems and problems such as cognitive overload and the disorientation of users, this project studies subject hypertext documents that have undergone conceptual structuring using facets for content representation and improvement of information retrieval during navigation. The main objective was to assess the possibility of the application of topic map technology for automating the compatibilization process of these structures. For this purpose, two dissertations from the UFMG Information Science Post-Graduation Program were adopted as samples. Both dissertations had been duly analyzed and structured on the MHTX (Hypertextual Map) prototype database. The faceted structures of both dissertations, which had been represented in conceptual maps, were then converted into topic maps. It was then possible to use the merge property of the topic maps to promote the semantic interrelationship between the maps and, consequently, between the hypertextual information resources proper. The merge results were then analyzed in the light of theories dealing with the compatibilization of languages developed within the realm of information technology and librarianship from the 1960s on. The main goals accomplished were: (a) the detailed conceptualization of the merge process of the topic maps, considering the possible compatibilization levels and the applicability of this technology in the integration of faceted structures; and (b) the production of a detailed sequence of steps that may be used in the implementation of topic maps based on faceted structures.
    Date
    22. 2.2013 11:39:23
  2. 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.
  3. 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.
  4. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.02 
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    Date
    13.12.2017 14:17:22
  5. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.02 
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    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  6. El idrissi esserhrouchni, O. et al.; Frikh, B.; Ouhbi, B.: OntologyLine : a new framework for learning non-taxonomic relations of domain ontology (2016) 0.01 
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  7. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01 
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  8. Öttl, S.; Streiff, D.; Stettler, N.; Studer, M.: Aufbau einer Testumgebung zur Ermittlung signifikanter Parameter bei der Ontologieabfrage (2010) 0.01 
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    Abstract
    Der Einsatz von semantischen Technologien ist mittlerweile ein etabliertes Mittel zur Optimierung von Information-Retrieval-Systemen. Obwohl der Einsatz von Ontologien für verschiedene Anwendungsbereiche wie beispielsweise zur Query-Expansion (Bhogal et al. 2007), zur Strukturierung von Benutzeroberflächen bzw. zur Dialoggestaltung (z. B. Garcia & Sicilia 2003; Liu et al. 2005; Lopez et al. 2006; Paulheim 2009; Paulheim & Probst 2010), in Recommendersystemen (z. B. Taehee et al. 2006; Cantador et al. 2008; Middleton et al. 2001; Middleton et al. 2009) usw. rege erforscht wird, gibt es noch kaum Bestrebungen, die einzelnen Abfragemethodiken für Ontologien systematisch zu untersuchen. Bei der Abfrage von Ontologien geht es in erster Linie darum, Zusammenhänge zwischen Begriffen zu ermitteln, indem hierarchische (Classes und Individuals), semantische (Object Properties) und ergänzende (Datatype Properties) Beziehungen abgefragt oder logische Verknüpfungen abgeleitet werden. Hierbei werden sogenannte Reasoner für die Ableitungen und als Abfragesprache SPARQL (seltener auch XPath) eingesetzt. Ein weiterer, weniger oft eingesetzter, vielversprechender Ansatz findet sich bei Hoser et al. (2006) und Weng & Chang (2008), die Techniken der Sozialen Netzwerkanalyse zur Auswertung von Ontologien miteinsetzen (Semantic Network Analysis). Um die Abfrage von Ontologien sowie Kombinationen der unterschiedlichen Abfragemöglichkeiten systematisch untersuchen zu können, wurde am SII eine entsprechende Testumgebung entwickelt, die in diesem Beitrag genauer vorgestellt werden soll.
  9. Semantische Technologien : Grundlagen - Konzepte - Anwendungen (2012) 0.01 
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    Content
    Inhalt: 1. Einleitung (A. Dengel, A. Bernardi) 2. Wissensrepräsentation (A. Dengel, A. Bernardi, L. van Elst) 3. Semantische Netze, Thesauri und Topic Maps (O. Rostanin, G. Weber) 4. Das Ressource Description Framework (T. Roth-Berghofer) 5. Ontologien und Ontologie-Abgleich in verteilten Informationssystemen (L. van Elst) 6. Anfragesprachen und Reasoning (M. Sintek) 7. Linked Open Data, Semantic Web Datensätze (G.A. Grimnes, O. Hartig, M. Kiesel, M. Liwicki) 8. Semantik in der Informationsextraktion (B. Adrian, B. Endres-Niggemeyer) 9. Semantische Suche (K. Schumacher, B. Forcher, T. Tran) 10. Erklärungsfähigkeit semantischer Systeme (B. Forcher, T. Roth-Berghofer, S. Agne) 11. Semantische Webservices zur Steuerung von Prooduktionsprozessen (M. Loskyll, J. Schlick, S. Hodeck, L. Ollinger, C. Maxeiner) 12. Wissensarbeit am Desktop (S. Schwarz, H. Maus, M. Kiesel, L. Sauermann) 13. Semantische Suche für medizinische Bilder (MEDICO) (M. Möller, M. Sintek) 14. Semantische Musikempfehlungen (S. Baumann, A. Passant) 15. Optimierung von Instandhaltungsprozessen durch Semantische Technologien (P. Stephan, M. Loskyll, C. Stahl, J. Schlick)
  10. Blobel, B.: Ontologies, knowledge representation, artificial intelligence : hype or prerequisite for international pHealth interoperability? (2011) 0.01 
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  11. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.01 
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    Date
    22. 7.2010 19:41:16
  12. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (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
  13. Madalli, D.P.; Balaji, B.P.; Sarangi, A.K.: Music domain analysis for building faceted ontological representation (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
  14. Girju, R.; Beamer, B.; Rozovskaya, A.; Fister, A.; Bhat, S.: ¬A knowledge-rich approach to identifying semantic relations between nominals (2010) 0.01 
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  15. Glimm, B.; Hogan, A.; Krötzsch, M.; Polleres, A.: OWL: Yet to arrive on the Web of Data? (2012) 0.01 
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  16. Muljarto, A.-R.; Salmon, J.-M.; Neveu, P.; Charnomordic, B.; Buche, P.: Ontology-based model for food transformation processes : application to winemaking (2014) 0.01 
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  17. Arenas, M.; Cuenca Grau, B.; Kharlamov, E.; Marciuska, S.; Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data (2014) 0.01 
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  18. 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.
  19. Tang, X.-B.; Wei Wei, G,-C.L.; Zhu, J.: ¬An inference model of medical insurance fraud detection : based on ontology and SWRL (2017) 0.01 
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  20. Hohmann, G.: ¬Die Anwendung des CIDOC-CRM für die semantische Wissensrepräsentation in den Kulturwissenschaften (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

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