Search (91 results, page 2 of 5)

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  1. Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004) 0.01 
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  2. Blobel, B.: Ontologies, knowledge representation, artificial intelligence : hype or prerequisite for international pHealth interoperability? (2011) 0.01 
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  3. Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022) 0.01 
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  4. Priss, U.: Faceted information representation (2000) 0.01 
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    Date
    22. 1.2016 17:47:06
  5. 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
  6. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01 
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    Date
    12. 2.2011 17:35:22
  7. 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
  8. Priss, U.: Faceted knowledge representation (1999) 0.01 
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    Date
    22. 1.2016 17:30:31
  9. Fonseca, F.: ¬The double role of ontologies in information science research (2007) 0.01 
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    Abstract
    In philosophy, Ontology is the basic description of things in the world. In information science, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality. Ontologies have been proposed for validating both conceptual models and conceptual schemas; however, these roles are quite dissimilar. In this article, we show that ontologies can be better understood if we classify the different uses of the term as it appears in the literature. First, we explain Ontology (upper case O) as used in Philosophy. Then, we propose a differentiation between ontologies of information systems and ontologies for information systems. All three concepts have an important role in information science. We clarify the different meanings and uses of Ontology and ontologies through a comparison of research by Wand and Weber and by Guarino in ontology-driven information systems. The contributions of this article are twofold: (a) It provides a better understanding of what ontologies are, and (b) it explains the double role of ontologies in information science research.
  10. Kruk, S.R.; Westerki, A.; Kruk, E.: Architecture of semantic digital libraries (2009) 0.01 
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    Source
    Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel
  11. Kruk, S.R.; Cygan, M.; Gzella, A.; Woroniecki, T.; Dabrowski, M.: JeromeDL: the social semantic digital library (2009) 0.01 
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    Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel
  12. 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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  13. Wielinga, B.; Wielemaker, J.; Schreiber, G.; Assem, M. van: Methods for porting resources to the Semantic Web (2004) 0.01 
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  14. Glimm, B.; Hogan, A.; Krötzsch, M.; Polleres, A.: OWL: Yet to arrive on the Web of Data? (2012) 0.01 
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  15. 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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  16. 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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  17. 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.
  18. 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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  19. Bardhan, S.; Dutta, B.: ONCO: an ontology model for MOOC platforms (2022) 0.01 
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  20. Definition of the CIDOC Conceptual Reference Model (2003) 0.01 
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    Date
    6. 8.2010 14:22:28

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