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  1. Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015) 0.07
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    Date
    22. 2.2017 12:51:57
  2. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.05
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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
  3. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.04
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    Date
    16.11.2018 14:22:01
  4. Assem, M. van; Rijgersberg, H.; Wigham, M.; Top, J.: Converting and annotating quantitative data tables (2010) 0.02
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    Abstract
    Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format diffiult. For example, the table header cell "f(Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.
    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  5. Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011) 0.02
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  6. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.02
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  7. Sojka, P.; Liska, M.: ¬The art of mathematics retrieval (2011) 0.02
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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
  8. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012) 0.01
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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.
  9. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01
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    Date
    17.12.2013 11:02:22
  10. Halpin, H.; Hayes, P.J.; McCusker, J.P.; McGuinness, D.L.; Thompson, H.S.: When owl:sameAs isn't the same : an analysis of identity in linked data (2010) 0.01
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    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  11. Líska, M.; Sojka, P.: MIaS 1.5 (2014) 0.01
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    Abstract
    A math-aware, full-text indexing based search engine that enables users to search for mathematical formulae inside documents. Search engine is unique because it is able to index and search structural information like representation of mathematical formulae. There is no other software or IR system that is able to store three billions of formulae in its index and search it with response time below a second. MIaS processes documents containing mathematical notation in MathML format. The system is built as an extension to any full-text indexing engine and has been verifiend on state-of-the-art Lucene core. It is scalable - it was verified to index almost whole arxiv.org (440,000 papers) having more than 160,000,000 formulae. Software is being used in EuDML (eudml.org) and other digital libraries. For more details see papers in peer reviewed conferences: [1] Sojka, Petr; Líska, Martin. In Matthew R. B. Hardy, Frank Wm. Tompa. Proceedings of the 2011 ACM Symposium on Document Engineering. Mountain View, CA, USA : ACM, 2011. pp.57--60. [2] Sojka, Petr; Líska, Martin. In J.H.Davenport, W.M. Farmer, J.Urban, F. Rabe. Intelligent Computer Mathematics LNCS 6824. Springer, 2011, pp.228--243.
  12. Niccolucci, F.: Linking theory with practice : CIDOC CRM-based gazetteers and time-period thesauri (2015) 0.01
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  13. Maltese, V.; Farazi, F.: Towards the integration of knowledge organization systems with the linked data cloud (2011) 0.01
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  14. Giunchiglia, F.; Maltese, V.; Dutta, B.: Domains and context : first steps towards managing diversity in knowledge (2011) 0.01
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  15. Bast, H.; Bäurle, F.; Buchhold, B.; Haussmann, E.: Broccoli: semantic full-text search at your fingertips (2012) 0.01
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  16. Momeni, F.; Mayr, P.: Analyzing the research output presented at European Networked Knowledge Organization Systems workshops (2000-2015) (2016) 0.01
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  17. Zhao, Y.; Ma, F.; Xia, X.: Evaluating the coverage of entities in knowledge graphs behind general web search engines : Poster (2017) 0.01
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  18. Mai, F.; Galke, L.; Scherp, A.: Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text (2018) 0.01
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  19. 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
  20. Delsey, T.: ¬The Making of RDA (2016) 0.01
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    Date
    17. 5.2016 19:22:40

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