Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Analytical semantics visualization for discovering latent signals in large text collections

: Stab, Christian; Breyer, Matthias; Burkhardt, Dirk; Nazemi, Kawa; Kohlhammer, Jörn

Volltext (PDF; )

Kerren, Andreas (Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-, Swedish Chapter:
SIGRAD 2012, Interactive Visual Analysis of Data. Proceedings : Växjö, Sweden, November 29-30, 2012
Linköping: Linköping University, 2012 (Linköping Electronic Conference Proceedings 81)
ISBN: 978-91-7519-723-4
ISSN: 1650-3686
SIGRAD Conference <11, 2012, Växjö/Sweden>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IGD ()
semantic visualization; trend analysis; information visualization; data mining; business intelligence; Business Field: Visual decision support; Business Field: Digital society; Research Area: Generalized digital documents

Considering the increasing pressure of competition and high dynamics of markets, the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today, those signals are encoded in large amounts of textual data like competitors' web sites, news articles, scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools, it is almost impossible to keep track of each thread or opportunity.
The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools, it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.