-
Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014)
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- Source
- http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
-
Networked Knowledge Organisation Systems and Services - TPDL 2011 : The 10th European Networked Knowledge Organisation Systems (NKOS) Workshop (2011)
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- Content
- Programm mit Links auf die Präsentationen: Armando Stellato, Ahsan Morshed, Gudrun Johannsen, Yves Jacques, Caterina Caracciolo, Sachit Rajbhandari, Imma Subirats, Johannes Keizer: A Collaborative Framework for Managing and Publishing KOS - Christian Mader, Bernhard Haslhofer: Quality Criteria for Controlled Web Vocabularies - Ahsan Morshed, Benjamin Zapilko, Gudrun Johannsen, Philipp Mayr, Johannes Keizer: Evaluating approaches to automatically match thesauri from different domains for Linked Open Data - Johan De Smedt: SKOS extensions to cover mapping requirements - Mark Tomko: Translating biological data sets Into Linked Data - Daniel Kless: Ontologies and thesauri - similarities and differences - Antoine Isaac, Jacco van Ossenbruggen: Europeana and semantic alignment of vocabularies - Douglas Tudhope: Complementary use of ontologies and (other) KOS - Wilko van Hoek, Brigitte Mathiak, Philipp Mayr, Sascha Schüller: Comparing the accuracy of the semantic similarity provided by the Normalized Google Distance (NGD) and the Search Term Recommender (STR) - Denise Bedford: Selecting and Weighting Semantically Discovered Concepts as Social Tags - Stella Dextre Clarke, Johan De Smedt. ISO 25964-1: a new standard for development of thesauri and exchange of thesaurus data
-
EuropeanaTech and Multilinguality : Issue 1 of EuropeanaTech Insight (2015)
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- Abstract
- Welcome to the very first issue of EuropeanaTech Insight, a multimedia publication about research and development within the EuropeanaTech community. EuropeanaTech is a very active community. It spans all of Europe and is made up of technical experts from the various disciplines within digital cultural heritage. At any given moment, members can be found presenting their work in project meetings, seminars and conferences around the world. Now, through EuropeanaTech Insight, we can share that inspiring work with the whole community. In our first three issues, we're showcasing topics discussed at the EuropeanaTech 2015 Conference, an exciting event that gave rise to lots of innovative ideas and fruitful conversations on the themes of data quality, data modelling, open data, data re-use, multilingualism and discovery. Welcome, bienvenue, bienvenido, Välkommen, Tervetuloa to the first Issue of EuropeanaTech Insight. Are we talking your language? No? Well I can guarantee you Europeana is. One of the European Union's great beauties and strengths is its diversity. That diversity is perhaps most evident in the 24 different languages spoken in the EU. Making it possible for all European citizens to easily and seamlessly communicate in their native language with others who do not speak that language is a huge technical undertaking. Translating documents, news, speeches and historical texts was once exclusively done manually. Clearly, that takes a huge amount of time and resources and means that not everything can be translated... However, with the advances in machine and automatic translation, it's becoming more possible to provide instant and pretty accurate translations. Europeana provides access to over 40 million digitised cultural heritage offering content in over 33 languages. But what value does Europeana provide if people can only find results in their native language? None. That's why the EuropeanaTech community is collectively working towards making it more possible for everyone to discover our collections in their native language. In this issue of EuropeanaTech Insight, we hear from community members who are making great strides in machine translation and enrichment tools to help improve not only access to data, but also how we retrieve, browse and understand it.
-
Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014)
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- Content
- "People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.
-
Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015)
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- Date
- 22. 2.2017 12:51:57
-
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
-
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.
-
Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013)
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- Date
- 17.12.2013 11:02:22
-
Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010)
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- Date
- 26.12.2011 13:40:22
-
Delsey, T.: ¬The Making of RDA (2016)
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- Date
- 17. 5.2016 19:22:40
-
Voß, J.: Classification of knowledge organization systems with Wikidata (2016)
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- Pages
- S.15-22
-
Zanibbi, R.; Yuan, B.: Keyword and image-based retrieval for mathematical expressions (2011)
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- Date
- 22. 2.2017 12:53:49
-
Open MIND (2015)
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- Date
- 27. 1.2015 11:48:22
-
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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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
-
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.
