https://puma.ub.uni-stuttgart.de/group/researchcode/tools%20visualization%20knowledgegraphPUMA RSS feed for /group/researchcode/tools%20visualization%20knowledgegraph2026-04-16T17:18:32+02:00https://puma.ub.uni-stuttgart.de/bibtex/209f16e8fe25f861e62651d003b10cb6c/diglezakisdiglezakis2024-12-06T13:34:57+01:00visualization tools knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rungsiman Nararatwong" itemprop="url" href="/person/1b8943f68661a4ae10db86dcb925d6494/author/0"><span itemprop="name">R. Nararatwong</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Natthawut Kertkeidkachorn" itemprop="url" href="/person/1b8943f68661a4ae10db86dcb925d6494/author/1"><span itemprop="name">N. Kertkeidkachorn</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ryutaro Ichise" itemprop="url" href="/person/1b8943f68661a4ae10db86dcb925d6494/author/2"><span itemprop="name">R. Ichise</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)</span>, </em></span><em>Seite <span itemprop="pagination">174-178</span>. </em>(<em><span>Dezember 2020<meta content="Dezember 2020" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 13:34:57 CET 20242020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)12174-178Knowledge Graph Visualization: Challenges, Framework, and Implementation2020visualization tools knowledgegraph 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.Knowledge Graph Visualization: Challenges, Framework, and Implementation | IEEE Conference Publication | IEEE Xplore