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Sperber, W.; Wegner, B.: Content Analysis in der Mathematik : Erschließung und Retrieval mathematischer Publikationen (2010)
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Göbel, S.; Sperber, W.; Wegner, B.: 150 Jahre : ein Rückblick auf das Jahrbuch über die Fortschritte der Mathematik (2020)
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Sperber, W.; Ion, P.D.F.: Content analysis and classification in mathematics (2011)
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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.
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Sperber, W.; Schöneberg, U.: Machine-learning methods for classification and content authority control in mathematics (2015)
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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.
