VISUALIZING RDF AND KNOWLEDGE GRAPHS INTERACTIVE FRAMEWORK TO SUPPORT ANALYSIS DECISION

Main Article Content

HATEM AHMED SAYED AHMED SOLIMAN
AHMAD TABAK

Abstract

Knowledge graphs are progressively important source of data and context information in many fields especially in Data Science; there is no doubt that the first step in data analysis is data exploration in which visualization plays an important role; Data visualization has become significant research challenge involving several issues related to storing, querying, indexing, visual presentation, interaction data [1]. The Semantic Web Resource Description Framework (RDF) describes metadata that aims to make the Web content not only machine-readable but also machine-understandable; this paper outline of Graph-based Visualization Systems overview and proposes Visualizing interactive RDF and knowledge Graphs Framework to support analysis decision.

Keywords:
Analytics, big data, interaction, RDF, visualization.

Article Details

How to Cite
AHMED SOLIMAN, H. A. S., & TABAK, A. (2020). VISUALIZING RDF AND KNOWLEDGE GRAPHS INTERACTIVE FRAMEWORK TO SUPPORT ANALYSIS DECISION. Journal of Global Economics, Management and Business Research, 12(1), 43-46. Retrieved from http://www.ikprress.org/index.php/JGEMBR/article/view/4920
Section
Original Research Article

References

Shneiderman B. Extreme visualization: Squeezing a billion records into a million pixels. In ACM Conference on Management of Data (SIGMOD); 2008.

Dadzie AS, Rowe M. Approaches to visualizing linked data. Semantic Web. 2011;2(2):89-124.

Brunetti J, Gil R, Garcia R. Facets and pivoting for exible and usable linked data exploration. Crete, Greece; 2012.

Frasincar F, Telea R, Houben GJ. Adapting graph visualization techniques for the visualization of RDF data. In Visualizing the Semantic Web; 2006.

Karger D, Schraefel M. The pathetic fallacy of RDF. Position Paper for SWUI06; 2006.

Chi EH. A taxonomy of visualization techniques using the data state reference model. In IEEE Symposium on Information Visualization 2000, INFOVIS '00, Washington, DC, USA, IEEE; 2000.

Thellmann K, Galkin M, Orlandi F, Auer S. LinkDaViz - Automatic binding of linked data to visualizations. In ISWC; 2015.

Battle L, Chang R, Stonebraker M. Dynamic prefetching of data tiles for interactive visualization. Technical Report; 2015.

Wongsuphasawat K, Moritz D, Anand A, Mackinlay JD, Howe B, Heer J. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. TVCG. 2016;22(1).

Park Y, Cafarella MJ, Mozafari B. Visualization-Aware sampling for very large databases. In ICDE; 2016.

Angles R, Gutierrez C. Querying RDF data from a graph database perspective. 2nd European Semantic Web Conference (ESWC), Greece. 2000;346-360.

Frasincar F, Telea A, Houben GJ. Adapting graph visualization techniques for the visualization of RDF data. Visualizing the Semantic Web. 2006;154–171.

White RW, Kules B, Drucker SM, Schraefel M. Supporting exploratory search. Communications of the ACM. 2006;49(4).

Deligiannidis Leonidas, Kochut Krys, Sheth Amit. RDF data exploration and visualization. 2007;39-46.
DOI: 10.1145/1317353.1317362

Auber D. Tulip - A huge graph visualization framework. In Graph Drawing Software; 2004.

Dimitriadou K, Papaemmanouil O, Diao Y. Explore-by-example: An automatic query steering framework for interactive data exploration. In SIGMOD; 2014.

Eldawy A, Mokbel M, Jonathan C. HadoopViz: A MapReduce framework for extensible visualization of big spatial data. In ICDE; 2016.

Soliman HASA, Tabak F. Deep learning framework for RDF and knowledge graphs using fuzzy maps to support medical decision. Journal of International Research in Medical and Pharmaceutical Sciences. 2020;14(3):92–97.
Available:http://www.ikprress.org/index.php/JIRMEPS/article/view/4893

Soliman H, Khalifa Z, Saleh MM. E-learning influence on the performance of primary school students. Journal of Global Research in Education and Social Science. 2019;13(3):84-88.
Available:http://ikprress.org/index.php/JOGRESS/article/view/4527

Soliman HASA. Storing RDF data: A brief survey. Journal of Basic and Applied Research International. 2019;25(6):344–347.
Available:http://www.ikprress.org/index.php/JOBARI/article/view/4846