Factographic data multidimensional search models
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Factographic data multidimensional search models
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| Creator |
Tertyshnyi, V.
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| Contributor |
Kremenchuk Mykhailo Ostohradskyi National University
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| Subject |
factographic search
multi-dimensional semantic space logical linguistic model |
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| Description |
The investigation objective is to simplify factographic data multidimensional search process by modifying latent-semantic analysis models and a corresponding logical linguistic model. Formal multidimensional semantic space model was created, the types of logical connections that can be used for a multidimensional factographic search were identified. Indicators and metrics for context clustering were chosen. Logical linguistic model for factographic data identification was formed.
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| Date |
2018-04-10T08:05:23Z
2018-04-10T08:05:23Z 2016 |
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| Type |
Conference Abstract
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| Identifier |
Tertyshnyi V. Factographic data multidimensional search models / V. Tertyshnyi // Litteris et Artibus : proceedings of the 6th International youth science forum, November 24–26, 2016, Lviv, Ukraine / Lviv Polytechnic National University. – Lviv : Lviv Polytechnic Publishing House, 2016. – P. 37–38. – Bibliography: 11 titles.
http://ena.lp.edu.ua:8080/handle/ntb/40242 |
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| Language |
en
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| Relation |
[1] Gries, S. Th. Corpus-based methods and cognitive semantics: the many meanings of to run / S. Th. Gries. – Corpora in cognitive linguistics: corpus-based approaches to syntax and lexis, 2006. – P. 57–99. [2] Evans, V. Lexical concepts, cognitive models and meaning-construction / V. Evans // Journal of Cognitive semiotics. – 2006. – P. 73-107. [3] Rio Blanco, Peter Milka, Sebatiano Vigna (2011) “Effective and Efficient Entity Search in RDF data”. The Semantic Web – ISWC – Springer, 92 p. [4] Guha, R. V. Semantic search / R. V. Guha, R. McCool, E. Miller // Proc. of the 12th inter. WWW conf. (WWW 2003). – Budapest, Hungary, 2003. – pp. 700-709. [5] Tao Cheng, Kevin Chen-Chuan Chang (2007) “Entity Search Engine: Towards Agile Best-Effort Information Integration over the Web”. CIDR –pp. 108-113. [6] Pedersen, T. Measures of semantic similarity and relatedness in the medical domain / T. Pedersen, S. Pakhamov, S. Patwardhan // University of Minnesota digital technology center research report DTC 2005/12. [7] Resnik, P. Semantic similarity in a taxonomy: An information-based measures and its application to problems of ambiguity in natural language / P. Resnik // Journal of artificial intelligence. – 1999. – pp. 95-130. [8] Shah, U. Information Retrieval on the Semantic Web / U. Shah, T. Finin, A. Joshi, R. Cost, J. Mayfield // 10th Inter. Conf.. on Information and Knowledge Management. – N.Y., USA: ACM Press, 2003. – pp. 461-68. [9] Sparck, J. Document Retrieval: Shallow Data, Deep Theories, Historical Reflections, Potential Directions / J. Sparck // 25 th European Conf. on IR Research. – Pisa, Italy: Springer Verlag, 2003. – V. 2633, № 77. – pp. 1-11. [10] Tsinaraki, С Ontology-Based Semantic Indexing for MPEG-7 and TV-Anytime Audiovisual Content / C. Tsinaraki, P. Polydoros, F. Kazasis // Multimedia Tools and Applications. – 2005. – V. 26. pp. 299-325. [11] Baziz, M. Semantic cores for representing documents in information retrieval / M. Baziz, M. Boughanem, N. Aussenac-Gilles, C. Chrisment // In Proc. Of 2005 ACM symposium on applied computing. – New Mexico, 2005. – pp. 1011-1017.
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| Format |
37-38
application/pdf |
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| Coverage |
UA
Lviv |
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| Publisher |
Lviv Polytechnic Publishing House
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