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  • × author_ss:"Sperber, W."
  • × year_i:[2010 TO 2020}
  1. Sperber, W.; Wegner, B.: Content Analysis in der Mathematik : Erschließung und Retrieval mathematischer Publikationen (2010) 0.02 
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    Abstract
    Die traditionellen Informationsdienste in den Wissenschaften stehen angesichts der Publikationsflut und der Entwicklung der elektronischen Medien gerade auch in der Mathematik vor großen Herausforderungen. Es müssen neue Konzepte für eine erweiterte qualitativ hochwertige Erschließung entwickelt werden, die weitgehend automatisierbar sind. In dem Beitrag werden die Konzepte zur Texterschließung in der Mathematik analysiert und Methoden entwickelt, die neue Möglichkeiten für das Retrieval eröffnen. Der Beitrag schließt mit einem Ausblick auf die Möglichkeiten zur Formel-Analyse.
    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
  2. Göbel, S.; Sperber, W.: Bibliographische Klassifikationen in der Mathematik : Werkzeuge der inhaltlichen Erschließung und für das Retrieval (2010) 0.02 
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  3. Greuel, G.-M.; Sperber, W.: swMATH - ein neuer Service für die Suche nach mathematischer Software (2013) 0.01 
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    Abstract
    Anwendungen der Mathematik ohne Computer, Software und Simulationen sind heute eigentlich kaum mehr vorstellbar, und die Entwicklung von Algorithmen und Softwarelösungen macht einen stetig wachsenden Teil der mathematischen Forschung aus. Aber anders als mathematische Literatur, wird mathematische Software nicht systematisch in Bibliotheken vorgehalten bzw. elektronisch nachgewiesen. Der vorliegende Beitrag stellt mit der open access Datenbank swMATH (http://www.swmath.org) einen neuen Ansatz vor, der bereits als Prototyp existiert und genutzt werden kann. Das Projekt swMATH wird von der Leibniz-Gemeinschaft gefördert und vom Mathematischen Forschungsinstitut Oberwolfach in Kooperation mit FIZ Karlsruhe bearbeitet. Fachliche Unterstützung erhält das Projekt vom DFG-Forschungszentrum MATHEON, vom Zuse-Institut Berlin, vom Weierstraß-Institut für Angewandte Analysis und Stochastik sowie dem Felix-Klein-Zentrum für Mathematik der Universität Kaiserslautern. zbMATH knüpft an den Dienst Oberwolfach References on Mathematical Software (ORMS) (http://orms.mfo.de) an. ORMS bietet differenzierte, manuell erstellte Informationen für eine relativ kleine Anzahl handverlesener mathematischer Softwaresysteme. swMATH arbeitet hingegen weitgehend mit automatischen Methoden, um möglichst viele mathematische Softwarelösungen verfügbar zu machen.
  4. Schöneberg, U.; Sperber, W.: POS tagging and its applications for mathematics (2014) 0.00 
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    Abstract
    Content analysis of scientific publications is a nontrivial task, but a useful and important one for scientific information services. In the Gutenberg era it was a domain of human experts; in the digital age many machine-based methods, e.g., graph analysis tools and machine-learning techniques, have been developed for it. Natural Language Processing (NLP) is a powerful machine-learning approach to semiautomatic speech and language processing, which is also applicable to mathematics. The well established methods of NLP have to be adjusted for the special needs of mathematics, in particular for handling mathematical formulae. We demonstrate a mathematics-aware part of speech tagger and give a short overview about our adaptation of NLP methods for mathematical publications. We show the use of the tools developed for key phrase extraction and classification in the database zbMATH.
    Series
    Lecture notes in computer science; 8543)(Lecture notes in artificial intelligence
  5. Sperber, W.; Ion, P.D.F.: Content analysis and classification in mathematics (2011) 0.00 
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    Abstract
    The number of publications in mathematics increases faster each year. Presently far more than 100,000 mathematically relevant journal articles and books are published annually. Efficient and high-quality content analysis of this material is important for mathematical bibliographic services such as ZBMath or MathSciNet. Content analysis has different facets and levels: classification, keywords, abstracts and reviews, and (in the future) formula analysis. It is the opinion of the authors that the different levels have to be enhanced and combined using the methods and technology of the Semantic Web. In the presentation, the problems and deficits of the existing methods and tools, the state of the art and current activities are discussed. As a first step, the Mathematical Subject Classification Scheme (MSC), has been encoded with Simple Knowledge Organization System (SKOS) and Resource Description Framework (RDF) at its recent revision to MSC2010. The use of SKOS principally opens new possibilities for the enrichment and wider deployment of this classification scheme and for machine-based content analysis of mathematical publications.
  6. Lange, C.; Ion, P.; Dimou, A.; Bratsas, C.; Sperber, W.; Kohlhasel, M.; Antoniou, I.: Bringing mathematics towards the Web of Data : the case of the Mathematics Subject Classification (2012) 0.00 
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    Abstract
    The Mathematics Subject Classification (MSC), maintained by the American Mathematical Society's Mathematical Reviews (MR) and FIZ Karlsruhe's Zentralblatt für Mathematik (Zbl), is a scheme for classifying publications in mathematics according to their subjects. While it is widely used, its traditional, idiosyncratic conceptualization and representation requires custom implementations of search, query and annotation support. This did not encourage people to create and explore connections of mathematics to subjects of related domains (e.g. science), and it made the scheme hard to maintain. We have reimplemented the current version of MSC2010 as a Linked Open Dataset using SKOS and our focus is concentrated on turning it into the new MSC authority. This paper explains the motivation, and details of our design considerations and how we realized them in the implementation. We present in-the-field use cases and point out how e-science applications can take advantage of the MSC LOD set. We conclude with a roadmap for bootstrapping the presence of mathematical and mathematics-based science, technology, and engineering knowledge on the Web of Data, where it has been noticeably underrepresented so far, starting from MSC/SKOS as a seed.
    Series
    Lecture notes in computer science; 7295
    Source
    9th Extended Semantic Web Conference (ESWC), 2012-05-27/2012-05-31 in Hersonissos, Crete, Greece. Eds.: Elena Simperl et al
  7. Sperber, W.; Schöneberg, U.: Machine-learning methods for classification and content authority control in mathematics (2015) 0.00 
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    Abstract
    The abstracting and reviewing service zbMATH (zbMATH, 1931- ) is the most comprehensive bibliographic database of mathematical literature. The database uses reviews, keywords and classification for content analysis of mathematical publications. Controlled vocabularies and classification schemes are important for a uniform and standardised analysis of the content and precise information retrieval. Over the last few years, the zbMATH team has started developing machine-based concepts and tools to create controlled vocabularies and to improve the Mathematics Subject Classification (MSC) scheme. Concepts of natural language processing and other machine learning methods, especially neural networks, were adapted to the specific requirements of mathematical information, e.g., named mathematical entities and mathematical formulas. The tools are used for key phrase extraction and classification of mathematical publications. Basing on the extracted key phrases, a prototype for a controlled vocabulary for mathematics was created. The tools and the state of the art are described briefly. These activities will help - in cooperation with authority control for authors, series and institutions - to automate the zbMATH workflow and improve the usefulness and information retrieval capabilities of the database.
    Content
    Präsentation für The International UDC Seminar entitled "Classification & Authority Control: Expanding Resource Discovery" took place in the National Library of Portugal in Lisbon, on 29-30 October 2015. Vgl.: http://www.udcds.com/seminar/2015/media/slides/Sperber_InternationalUDCSeminar2015.pdf.
  8. Schöneberg, U.; Sperber, W.: ¬The DeLiVerMATH project : text analysis in mathematics (2013) 0.00 
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    Series
    Lecture notes in computer science; vol. 7961
  9. Lange, C.; Ion, P.; Dimou, A.; Bratsas, C.; Sperber, W.; Kohlhasel, M.; Antoniou, I.: Getting mathematics towards the Web of Data : the case of the Mathematics Subject Classification (2012) 0.00 
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    Abstract
    The Mathematics Subject Classification (MSC), maintained by the American Mathematical Society's Mathematical Reviews (MR) and FIZ Karlsruhe's Zentralblatt für Mathematik (Zbl), is a scheme for classifying publications in mathematics according to their subjects. While it is widely used, its traditional, idiosyncratic conceptualization and representation requires custom implementations of search, query and annotation support. This did not encourage people to create and explore connections of mathematics to subjects of related domains (e.g. science), and it made the scheme hard to maintain. We have reimplemented the current version of MSC2010 as a Linked Open Dataset using SKOS and our focus is concentrated on turning it into the new MSC authority. This paper explains the motivation, and details of our design considerations and how we realized them in the implementation. We present in-the-field use cases and point out how e-science applications can take advantage of the MSC LOD set. We conclude with a roadmap for bootstrapping the presence of mathematical and mathematics-based science, technology, and engineering knowledge on the Web of Data, where it has been noticeably underrepresented so far, starting from MSC/SKOS as a seed.

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