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H-Maps: An Efficient Approach for Graphical Visualization and Navigation of Topic Maps and Data Representation in Computational Biology

Paper, was published by Markus Leber, Thilo Klöpfer, and Steffen Hüttner at 2009-11-12

The paper presents overviews the new tool H-Maps.

External Link: more information

Nowadays semantic networks become more important in industry and modern science. For this reason there is a need for an efficient tool allowing preparation and navigation through complex data spaces. We present the novel Topic Map tool H-Maps, which offers a web based navigator providing an enhanced utilization and graphical visualization of semantic networks. Present applications transform existing content to subject-centric Topic Map representations in a one-way fashion. Preserving the advantage of transferring diverse back ends into a unified topic map in existing applications the H-Maps approach goes one step further. The resulting topic map structure can be examined and extended with an iterative procedure. Especially in science this procedure is leading to a considerable improvement of flexibility and performance as can be shown within projects of computational biology.

Presented at

TMRA 2009

Conference in Leipzig from {{start}} to {{end}}

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Call for Contributions TMRA 2009 will be the fifth event in the annual series of international conferences on Topic Maps Research and …

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Projects

H-Maps

is a {{project}}.

Hmaps1_650

H-Maps ist die Software-Antwort der Hölle & Hüttner AG zur Darstellung Ihrer Daten- und Informationsstrukturen. Auf Basis des 1999 festegelegten ...

Visit homepage of H-Maps

glossary

TMDM

is associated with {{count}} items.

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TMDM is the abbreviation for Topic Maps Data Model. You will find a compact illustration of all relationships within the TMDM

GTM

is associated with {{count}} items.

Signet_glossary

The acronym GTM stands for Graphical Topic Maps notation. GTM is a graphical notation used to define ontologies (level 1) and represent Topic Maps instance data (level 0).

 

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The idea of Topic Maps is essential to enable dynamic information logistic. This requires a system that understands the context of the user to provide relevant informations and options automatically. Therefore semantic analysis is needed organizing content in a dynamic net structure.

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