-
Qin, J.; Paling, S.: Converting a controlled vocabulary into an ontology : the case of GEM (2001)
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- Date
- 24. 8.2005 19:20:22
-
Dowding, H.; Gengenbach, M.; Graham, B.; Meister, S.; Moran, J.; Peltzman, S.; Seifert, J.; Waugh, D.: OSS4EVA: using open-source tools to fulfill digital preservation requirements (2016)
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- Date
- 28.10.2016 18:22:33
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Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978)
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- https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
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Heflin, J.; Hendler, J.: Semantic interoperability on the Web (2000)
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- Date
- 11. 5.2013 19:22:18
-
Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023)
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- Date
- 29.12.2022 18:22:55
-
Sojka, P.; Liska, M.: ¬The art of mathematics retrieval (2011)
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- Content
- Vgl.: DocEng2011, September 19-22, 2011, Mountain View, California, USA Copyright 2011 ACM 978-1-4503-0863-2/11/09
- Date
- 22. 2.2017 13:00:42
-
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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-
Griesbaum, J.; Rittberger, M.; Bekavac, B.: Deutsche Suchmaschinen im Vergleich : AltaVista.de, Fireball.de, Google.de und Lycos.de (2002)
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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.
-
Lynch, J.D.; Gibson, J.; Han, M.-J.: Analyzing and normalizing type metadata for a large aggregated digital library (2020)
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-
Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012)
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- Abstract
- This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.
-
Voß, J.: Classification of knowledge organization systems with Wikidata (2016)
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- Pages
- S.15-22
-
Atran, S.; Medin, D.L.; Ross, N.: Evolution and devolution of knowledge : a tale of two biologies (2004)
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- Content
- Vgl.: https://www.researchgate.net/publication/5090125_Evolution_and_Devolution_of_Knowledge_A_Tale_of_Two_Biologies. Vgl. auch: 10.1111/j.1467-9655.2004.00195.x.
- Date
- 23. 1.2022 10:22:18
-
Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020)
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-
Wielinga, B.; Wielemaker, J.; Schreiber, G.; Assem, M. van: Methods for porting resources to the Semantic Web (2004)
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- Source
- Proceedings of the First European Semantic Web Symposium (ESWS2004), Eds.: C. Bussler, J. Davies, D. Fensel and R. Studer. 2004. S.299-311
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Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004)
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Apel, J.; Gebhart, F.; Maylein, L.; Wlotzka, M.: Offene Forschungsdaten an der Universität Heidelberg : von generischen institutionellen Repositorien zu fach- und projektspezifischen Diensten (2018)
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Tappenbeck, I.; Michel, A.; Wittich, A.; Werr, N.; Gäde, M.; Spree, U.; Gläser, C.; Griesbaum, J.; Mandl, T.; Keller-Loibl, K.; Stang, R.: Framework Informationskompetenz : Ein gemeinsamer Standard für die Qualifikation in den bibliotheks- und informationswissenschaftlichen Studiengängen in Deutschland (2022)
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Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020)
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Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006)
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- Date
- 3.12.2016 18:39:22
