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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/209f16e8fe25f861e62651d003b10cb6c/diglezakis",         
         "tags" : [
            "visualization","tools","knowledgegraph"
         ],
         
         "intraHash" : "09f16e8fe25f861e62651d003b10cb6c",
         "interHash" : "b8943f68661a4ae10db86dcb925d6494",
         "label" : "Knowledge Graph Visualization: Challenges, Framework, and Implementation",
         "user" : "diglezakis",
         "description" : "Knowledge Graph Visualization: Challenges, Framework, and Implementation | IEEE Conference Publication | IEEE Xplore",
         "date" : "2024-12-06 13:34:57",
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         "pub-type": "inproceedings",
         "booktitle": "2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)",
         "year": "2020", 
         "url": "", 
         
         "author": [ 
            "Rungsiman Nararatwong","Natthawut Kertkeidkachorn","Ryutaro Ichise"
         ],
         "authors": [
         	
            	{"first" : "Rungsiman",	"last" : "Nararatwong"},
            	{"first" : "Natthawut",	"last" : "Kertkeidkachorn"},
            	{"first" : "Ryutaro",	"last" : "Ichise"}
         ],
         "pages": "174-178","abstract": "A knowledge graph (KG) is a rich resource representing real-world facts. Visualizing a knowledge graph helps humans gain a deep understanding of the facts, leading to new insights and concepts. However, the massive and complex nature of knowledge graphs has brought many longstanding challenges, especially to attract non-expert users. This paper discusses these challenges; we turned them into a generic knowledge-graph visualization framework, namely KGViz, consisting of four dimensions: modularity, intuitive user interface, performance, and access control. Our implementation of KGViz is a high-capacity, extendable, and scalable KG visualizer, which we designed to promotes community contributions.",
         "doi" : "10.1109/AIKE48582.2020.00034",
         
         "bibtexKey": "9355442"

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