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Gorman, M.: From card catalogues to WebPACs : celebrating cataloguing in the 20th century (2000)
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Notess, M.: Three looks at users : a comparison of methods for studying digital library use (2004)
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Taube, M.; Gull, C.D.; Wachtel, I.S.: Unit terms in coordinate indexing (1952)
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Taube, M.; Wachtel, I.S.: ¬The logical structure of coordinate indexing (1953)
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Priss, U.: Description logic and faceted knowledge representation (1999)
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- Date
- 22. 1.2016 17:30:31
-
Delsey, T.: ¬The Making of RDA (2016)
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- Date
- 17. 5.2016 19:22:40
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Voß, J.: Classification of knowledge organization systems with Wikidata (2016)
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- Pages
- S.15-22
-
Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008)
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- Date
- 28.11.2016 12:43:22
-
Zanibbi, R.; Yuan, B.: Keyword and image-based retrieval for mathematical expressions (2011)
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- Date
- 22. 2.2017 12:53:49
-
Atran, S.; Medin, D.L.; Ross, N.: Evolution and devolution of knowledge : a tale of two biologies (2004)
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- Date
- 23. 1.2022 10:22:18
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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- Abstract
- In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
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Apps, A.; MacIntyre, R.; Heery, R.; Patel, M.; Salokhe, G.: Zetoc : a Dublin Core Based Current Awareness Service (2002)
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Greenberg, J.; Pattuelli, M.; Parsia, B.; Robertson, W.: Author-generated Dublin Core Metadata for Web Resources : A Baseline Study in an Organization (2002)
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Baeza-Yates, R.; Boldi, P.; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms (2006)
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- Date
- 16. 1.2016 10:22:28
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Roy, W.; Gray, C.: Preparing existing metadata for repository batch import : a recipe for a fickle food (2018)
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- Date
- 10.11.2018 16:27:22
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Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018)
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- Date
- 16.11.2018 14:22:01
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Gorman, M.: Revisiting enduring values (2015)
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- Footnote
- Bezugnahme auf das Buch: Gorman, M.: Our enduring values: librarianship in the 21st century. Chicago [u.a.] ; American Library Ass. ; 2000 ; IX, 188 S.
-
Manguinhas, H.; Charles, V.; Isaac, A.; Miles, T.; Lima, A.; Neroulidis, A.; Ginouves, V.; Atsidis, D.; Hildebrand, M.; Brinkerink, M.; Gordea, S.: Linking subject labels in cultural heritage metadata to MIMO vocabulary using CultuurLink (2016)
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Krötzsch, M.; Hitzler, P.; Ehrig, M.; Sure, Y.: Category theory in ontology research : concrete gain from an abstract approach (2004 (?))
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Daquino, M.; Peroni, S.; Shotton, D.; Colavizza, G.; Ghavimi, B.; Lauscher, A.; Mayr, P.; Romanello, M.; Zumstein, P.: ¬The OpenCitations Data Model (2020)
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