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Knowledge discovery in multidimensional knowledge representation framework

An integrative approach for the visualization of text analytics results

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Abstract

Visualization of results is one of the central challenges in big data analytics and integrative text mining. With a growing amount of unstructured data and different perspectives on big data, knowledge graphs have difficulties to simultaneously represent and visualize all analyzed dimensions of knowledge. This paper proposes integrative text mining as a solution to combine results from different dimensional analysis in a multidimensional knowledge representation (MKR) for knowledge discovery and visualization purpose. Analysis results from named entity recognition, topic detection, sentiment analysis and the extraction of semantic relationships are, therefore, integrated into a common representation structure. In the implementation part of this research, an application is introduced which utilizes MKR based on the results of stated text mining methods applied on a German and English news data set. State-of-the-art visualizations are used in the application and MKR adaptively transforms the visualization type of the knowledge graph according to the selected context for knowledge discovery.

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Notes

  1. https://www.wikidata.org/.

  2. http://rtw.ml.cmu.edu/rtw/.

  3. https://concept.research.microsoft.com/.

  4. https://www.google.com/intl/es419/insidesearch/.

  5. https://newsroom.fb.com/company-info/.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Institute of Knowledge Management and Knowledge Based Systems, University of Siegen, Siegen, Germany

    Johannes Zenkert, André Klahold & Madjid Fathi

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  1. Johannes Zenkert
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  2. André Klahold
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  3. Madjid Fathi
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Correspondence to Johannes Zenkert.

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Zenkert, J., Klahold, A. & Fathi, M. Knowledge discovery in multidimensional knowledge representation framework. Iran J Comput Sci 1, 199–216 (2018). https://doi.org/10.1007/s42044-018-0019-0

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