Search (26 results, page 1 of 2)

Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.03

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Date: 16.11.2018 14:22:01

Hitzler, P.; Janowicz, K.: Ontologies in a data driven world : finding the middle ground (2013) 0.02

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RDF Semantics (2004) 0.01

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Editor: Hayes, P. u. B. McBride

Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.01

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Date: 31. 7.2010 16:58:22

OWL Web Ontology Language Test Cases (2004) 0.01

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Date: 14. 8.2011 13:33:22

Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.01

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Content: Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

Scheir, P.; Pammer, V.; Lindstaedt, S.N.: Information retrieval on the Semantic Web : does it exist? (2007) 0.01

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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

Koutsomitropoulos, D.A.; Solomou, G.D.; Alexopoulos, A.D.; Papatheodorou, T.S.: Semantic metadata interoperability and inference-based querying in digital repositories (2009) 0.01

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Source: Journal of information technology research. 2(2009) no.4, p.37-53

Studer, R.; Studer, H.-P.; Studer, A.: Semantisches Knowledge Retrieval (2001) 0.01

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Hüsken, P.: Informationssuche im Semantic Web : Methoden des Information Retrieval für die Wissensrepräsentation (2006) 0.01

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Hüsken, P.: Information Retrieval im Semantic Web (2006) 0.01

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Fluit, C.; Horst, H. ter; Meer, J. van der; Sabou, M.; Mika, P.: Spectacle (2004) 0.01

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Iosif, V.; Mika, P.; Larsson, R.; Akkermans, H.: Field experimenting with Semantic Web tools in a virtual organization (2004) 0.01

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OWL Web Ontology Language Semantics and Abstract Syntax (2004) 0.01

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Editor: Patel-Schneider, P.F., P. Hayes u . I. Horrocks

Uren, V.; Cimiano, P.; Iria, J.; Handschuh, S.; Vargas-Vera, M.; Motta, E.; Ciravegnac, F.: Semantic annotation for knowledge management : requirements and a survey of the state of the art (2006) 0.01

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Gendt, M. van; Isaac, I.; Meij, L. van der; Schlobach, S.: Semantic Web techniques for multiple views on heterogeneous collections : a case study (2006) 0.01

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Source: Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006

Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.01

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Date: 26.12.2011 13:40:22

Prud'hommeaux, E.; Gayo, E.: RDF ventures to boldly meet your most pedestrian needs (2015) 0.01

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Source: Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

Fernández, M.; Cantador, I.; López, V.; Vallet, D.; Castells, P.; Motta, E.: Semantically enhanced Information Retrieval : an ontology-based approach (2011) 0.01
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Abstract

Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.

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