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<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="https://puma.ub.uni-stuttgart.de/group/researchcode/tools%20visualization%20knowledgegraph"><owl:Ontology rdf:about=""><rdfs:comment>PUMA publications for /group/researchcode/tools%20visualization%20knowledgegraph</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/209f16e8fe25f861e62651d003b10cb6c/diglezakis"><owl:sameAs rdf:resource="/uri/bibtex/209f16e8fe25f861e62651d003b10cb6c/diglezakis"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#InProceedings"/><swrc:date>Fri Dec 06 13:34:57 CET 2024</swrc:date><swrc:booktitle>2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)</swrc:booktitle><swrc:month>12</swrc:month><swrc:pages>174-178</swrc:pages><swrc:title>Knowledge Graph Visualization: Challenges, Framework, and Implementation</swrc:title><swrc:year>2020</swrc:year><swrc:keywords>visualization tools knowledgegraph </swrc:keywords><swrc: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.</swrc:abstract><swrc:hasExtraField><swrc:Field swrc:value="10.1109/AIKE48582.2020.00034" swrc:key="doi"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Rungsiman Nararatwong"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Natthawut Kertkeidkachorn"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Ryutaro Ichise"/></rdf:_3></rdf:Seq></swrc:author></rdf:Description></rdf:RDF>