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Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014)
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- Date
- 20. 1.2015 18:30:22
- Source
- Aslib journal of information management. 66(2014) no.6, S.678-696
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Mäkelä, E.; Hyvönen, E.; Saarela, S.; Vilfanen, K.: Application of ontology techniques to view-based semantic serach and browsing (2012)
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- Abstract
- We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.
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Green, R.: See-also relationships in the Dewey Decimal Classification (2011)
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- Source
- Knowledge organization. 38(2011) no.4, S.335-341
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Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014)
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- Pages
- S.108-113
-
Moreira, W.; Martínez-Ávila, D.: Concept relationships in knowledge organization systems : elements for analysis and common research among fields (2018)
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- Source
- Cataloging and classification quarterly. 56(2018) no.1, S.19-39
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Arenas, M.; Cuenca Grau, B.; Kharlamov, E.; Marciuska, S.; Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data (2014)
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-
Boteram, F.: Typisierung semantischer Relationen in integrierten Systemen der Wissensorganisation (2013)
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- Pages
- S.152-160
-
Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010)
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-
Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017)
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- Pages
- S.185-199
-
Koopman, B.; Zuccon, G.; Bruza, P.; Sitbon, L.; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search (2016)
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- Source
- Information Retrieval Journal. 19(2016) no.1, S.6-37
-
Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017)
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- Pages
- xix, 215 S
-
Hannech, A.: Système de recherche d'information étendue basé sur une projection multi-espaces (2018)
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- Abstract
- Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
- Pages
- 351 S
