Search (53 results, page 1 of 3)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Wissensrepräsentation"
  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) 0.09 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.08 
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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.
  3. Dietiker, S.: Cognitive Map einer Bibliothek : eine Überprüfung der Methodentauglichkeit im Bereich Bibliothekswissenschaft - am Beispiel der Kantonsbibliothek Graubünden (2016) 0.02 
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    Abstract
    Cognitive Maps sind mentale Karten, welche jeder Mensch besitzt. Es handelt sich dabei um eine Reflexion der jeweiligen Umwelt. Cognitive Mapping ist eine Methode, welche diese mentale Karte sichtbar macht. Aufgrund dieser Visualisierung können Erkenntnisse darüber gewonnen werden, was Menschen an einem Ort oder in einem Raum tun und wahrnehmen. Die Methode hat verschiede Anwendungstechniken, welche sich in sechs Kategorien teilen: Aufgabenlösung, Elemente orten, Sketch Map erstellen, Zonen und Gebiete einzeichnen, Weg- und Ortsbeschreibung sowie Kognitive Befragung. Anhand dieser lassen sich Untersuchungen beliebig kombinieren. Die Anwendung von Cognitive Mapping sowie eine einfache Befragung in der Kantonsbibliothek Graubünden hat ergeben, dass die Methode für Bibliotheken sinnvoll ist. Allerdings sollte bei zukünftigen Anwendungen die Punkte Gesamtaufwand, Untersuchungsaufbau, Teilnehmer-Zusammensetzung und Auswertungs-Aufwand angepasst werden.
  4. Riss, U.V.: Knowledge and action between abstraction and concretion (2014) 0.02 
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    Pages
    S.145-168
  5. 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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    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
  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. 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.
  8. 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)
  9. Blobel, B.: Ontologies, knowledge representation, artificial intelligence : hype or prerequisite for international pHealth interoperability? (2011) 0.01 
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  10. 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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  11. Glimm, B.; Hogan, A.; Krötzsch, M.; Polleres, A.: OWL: Yet to arrive on the Web of Data? (2012) 0.01 
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  12. 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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  13. 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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  14. 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.
  15. 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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  16. Fischer, W.; Bauer, B.: Combining ontologies and natural language (2010) 0.00 
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  17. Djioua, B.; Desclés, J.-P.; Alrahabi, M.: Searching and mining with semantic categories (2012) 0.00 
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  18. Bast, H.; Bäurle, F.; Buchhold, B.; Haussmann, E.: Broccoli: semantic full-text search at your fingertips (2012) 0.00 
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  19. Giunchiglia, F.; Dutta, B.; Maltese, V.: From knowledge organization to knowledge representation (2014) 0.00 
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  20. Kless, D.; Milton, S.; Kazmierczak, E.; Lindenthal, J.: Thesaurus and ontology structure : formal and pragmatic differences and similarities (2015) 0.00 
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    Abstract
    Thesauri and other types of controlled vocabularies are increasingly re-engineered into ontologies described using the Web Ontology Language (OWL), particularly in the life sciences. This has led to the perception by some that thesauri are ontologies once they are described by using the syntax of OWL while others have emphasized the need to re-engineer a vocabulary to use it as ontology. This confusion is rooted in different perceptions of what ontologies are and how they differ from other types of vocabularies. In this article, we rigorously examine the structural differences and similarities between thesauri and meaning-defining ontologies described in OWL. Specifically, we conduct (a) a conceptual comparison of thesauri and ontologies, and (b) a comparison of a specific thesaurus and a specific ontology in the same subject field. Our results show that thesauri and ontologies need to be treated as 2 orthogonal kinds of models with superficially similar structures. An ontology is not a good thesaurus, nor is a thesaurus a good ontology. A thesaurus requires significant structural and other content changes to become an ontology, and vice versa.

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