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Wang, S.; Isaac, A.; Schlobach, S.; Meij, L. van der; Schopman, B.: Instance-based semantic interoperability in the cultural heritage (2012)
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Bozzato, L.; Braghin, S.; Trombetta, A.: ¬A method and guidelines for the cooperation of ontologies and relational databases in Semantic Web applications (2012)
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Woolcott, L.; Payant, A.; Skindelien, S.: Partnering for discoverability : Knitting archival finding aids to digitized material using a low tech digital content linking process (2016)
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Rajan, L.: Ambiguity in knowledge organization : four proposed types (2017)
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Mai, F.; Galke, L.; Scherp, A.: Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text (2018)
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Wenige, L.; Ruhland, J.: Similarity-based knowledge graph queries for recommendation retrieval (2019)
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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
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Zanibbi, R.; Yuan, B.: Keyword and image-based retrieval for mathematical expressions (2011)
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- Date
- 22. 2.2017 12:53:49
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Open MIND (2015)
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- Date
- 27. 1.2015 11:48:22
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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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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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- 16.11.2018 14:22:01
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Somers, J.: Torching the modern-day library of Alexandria : somewhere at Google there is a database containing 25 million books and nobody is allowed to read them. (2017)
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- Abstract
- You were going to get one-click access to the full text of nearly every book that's ever been published. Books still in print you'd have to pay for, but everything else-a collection slated to grow larger than the holdings at the Library of Congress, Harvard, the University of Michigan, at any of the great national libraries of Europe-would have been available for free at terminals that were going to be placed in every local library that wanted one. At the terminal you were going to be able to search tens of millions of books and read every page of any book you found. You'd be able to highlight passages and make annotations and share them; for the first time, you'd be able to pinpoint an idea somewhere inside the vastness of the printed record, and send somebody straight to it with a link. Books would become as instantly available, searchable, copy-pasteable-as alive in the digital world-as web pages. It was to be the realization of a long-held dream. "The universal library has been talked about for millennia," Richard Ovenden, the head of Oxford's Bodleian Libraries, has said. "It was possible to think in the Renaissance that you might be able to amass the whole of published knowledge in a single room or a single institution." In the spring of 2011, it seemed we'd amassed it in a terminal small enough to fit on a desk. "This is a watershed event and can serve as a catalyst for the reinvention of education, research, and intellectual life," one eager observer wrote at the time. On March 22 of that year, however, the legal agreement that would have unlocked a century's worth of books and peppered the country with access terminals to a universal library was rejected under Rule 23(e)(2) of the Federal Rules of Civil Procedure by the U.S. District Court for the Southern District of New York. When the library at Alexandria burned it was said to be an "international catastrophe." When the most significant humanities project of our time was dismantled in court, the scholars, archivists, and librarians who'd had a hand in its undoing breathed a sigh of relief, for they believed, at the time, that they had narrowly averted disaster.
-
Markoff, J.: Researchers announce advance in image-recognition software (2014)
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- Content
- Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""
-
Vocht, L. De: Exploring semantic relationships in the Web of Data : Semantische relaties verkennen in data op het web (2017)
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