https://puma.ub.uni-stuttgart.de/group/researchcode/visualization%20knowledgegraphPUMA RSS feed for /group/researchcode/visualization%20knowledgegraph2026-04-16T17:22:59+02:00https://puma.ub.uni-stuttgart.de/bibtex/207df060fbe500b5bb3466f07a5b6bc48/diglezakisdiglezakis2024-12-06T15:08:23+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Renato De Donato" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/0"><span itemprop="name">R. De Donato</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martina Garofalo" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/1"><span itemprop="name">M. Garofalo</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Delfina Malandrino" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/2"><span itemprop="name">D. Malandrino</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Angela Pellegrino" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/3"><span itemprop="name">M. Pellegrino</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrea Petta" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/4"><span itemprop="name">A. Petta</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vittorio Scarano" itemprop="url" href="/person/1aa331e6c339ea65b6a5447e485c35ef3/author/5"><span itemprop="name">V. Scarano</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Semantic Systems. In the Era of Knowledge Graphs</span>, </em></span><em>page <span itemprop="pagination">70--86</span>. </em><em>Cham, </em><em><span itemprop="publisher">Springer International Publishing</span>, </em>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 15:08:23 CET 2024ChamSemantic Systems. In the Era of Knowledge Graphs70--86QueDI: From Knowledge Graph Querying to Data Visualization2020visualization knowledgegraph While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on experts in table manipulation, such as OD experts) in querying and exploiting LOD by taking advantage of our target users' expertise in table manipulation and chart creation.QueDI: From Knowledge Graph Querying to Data Visualization | SpringerLinkhttps://puma.ub.uni-stuttgart.de/bibtex/2801deda933893126de28b597d97551b3/diglezakisdiglezakis2024-12-06T15:05:54+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jacqueline Peng" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/0"><span itemprop="name">J. Peng</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Xu" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/1"><span itemprop="name">D. Xu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ryan Lee" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/2"><span itemprop="name">R. Lee</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Siwei Xu" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/3"><span itemprop="name">S. Xu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yunyun Zhou" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/4"><span itemprop="name">Y. Zhou</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kai Wang" itemprop="url" href="/person/1ec3f108c32ef5a5004ab52ff4972b2cf/author/5"><span itemprop="name">K. Wang</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">BMC Medical Informatics and Decision Making</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">22 </span></span>(<span itemprop="issueNumber">2</span>): <span itemprop="pagination">147</span></em> </span>(<em><span>Jun 2, 2022<meta content="Jun 2, 2022" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 15:05:54 CET 2024BMC Medical Informatics and Decision Making062147Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology222022visualization knowledgegraph 02Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology | BMC Medical Informatics and Decision Makinghttps://puma.ub.uni-stuttgart.de/bibtex/20eaa007129732f6a1bb5a6343c3b3672/diglezakisdiglezakis2024-12-06T14:30:01+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Harry Li" itemprop="url" href="/person/1d00ae27590c0d416351002960b12fda4/author/0"><span itemprop="name">H. Li</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gabriel Appleby" itemprop="url" href="/person/1d00ae27590c0d416351002960b12fda4/author/1"><span itemprop="name">G. Appleby</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Camelia Daniela Brumar" itemprop="url" href="/person/1d00ae27590c0d416351002960b12fda4/author/2"><span itemprop="name">C. Brumar</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Remco Chang" itemprop="url" href="/person/1d00ae27590c0d416351002960b12fda4/author/3"><span itemprop="name">R. Chang</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ashley Suh" itemprop="url" href="/person/1d00ae27590c0d416351002960b12fda4/author/4"><span itemprop="name">A. Suh</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Transactions on Visualization and Computer Graphics</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">1</span>): <span itemprop="pagination">584-594</span></em> </span>(<em><span>January 2024<meta content="January 2024" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 14:30:01 CET 2024IEEE Transactions on Visualization and Computer Graphics011584-594Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities302024visualization knowledgegraph This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners – KG Builders, Analysts, and Consumers – each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities | IEEE Journals & Magazine | IEEE Xplorehttps://puma.ub.uni-stuttgart.de/bibtex/2ba54b710ac9b8984b3594c973b4a7563/diglezakisdiglezakis2024-12-06T14:18:30+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yamei Tu" itemprop="url" href="/person/128795cd0ac754c744efb4ce2842b4af6/author/0"><span itemprop="name">Y. Tu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rui Qiu" itemprop="url" href="/person/128795cd0ac754c744efb4ce2842b4af6/author/1"><span itemprop="name">R. Qiu</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Han-Wei Shen" itemprop="url" href="/person/128795cd0ac754c744efb4ce2842b4af6/author/2"><span itemprop="name">H. Shen</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">2024 IEEE 17th Pacific Visualization Conference (PacificVis)</span>, </em></span><em>page <span itemprop="pagination">162-171</span>. </em>(<em><span>April 2024<meta content="April 2024" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 14:18:30 CET 20242024 IEEE 17th Pacific Visualization Conference (PacificVis)04162-171KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations2024visualization knowledgegraph IEEE Transactions on Visualization and Computer Graphics (TVCG) publishes cutting-edge research in the fields of visualization, computer graphics, and virtual and augmented realities. Within the TVCG ecosystem, different stakeholders make decisions based on available information related to TVCG almost on a daily basis. The decisions involve various tasks such as the retrieval of research ideas and trends, the invitation of peer reviewers, and the selection of editorial board members, just to name a few. To make well-informed decisions in these contexts, a data-driven approach is necessary. However, the current IEEE digital library only provides access to individual papers. Transforming this wealth of data into valuable insights is a daunting task, requiring specialized expertise and effort in tasks such as data crawling, cleaning, analysis, and visualizations. To address the needs of the community in facilitating more efficient and transparent decision-making, we construct and publicly release a TVCG knowledge graph (TVCG-KG). TVCG-KG is a structured representation of heterogeneous information, including the metadata of each publication such as author, affiliation, title, and semantic information such as method, task, data. Despite the widespread use of KGs in various downstream applications, a noticeable gap exists in the visualization literature regarding the full exploitation of the rich semantics embedded within KGs. While it might seem intuitive to just employ interactive graph-based visualization for KGs, we propose that knowledge discovery over KG is a series of visual exploratory tasks that can benefit from using multiple visualization techniques and designs. We conducted an evaluation of TVCG-KG quality and demonstrated its practical utility through several real-world cases. Our data and code are accessible via the following URL: https://github.com/yasmineTYM/TVCG-KG.git.KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations | IEEE Conference Publication | IEEE Xplorehttps://puma.ub.uni-stuttgart.de/bibtex/2b2d9c12ca9bfcf955806b8451d0b8153/diglezakisdiglezakis2024-12-06T14:14:25+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thibault Ehrhart" itemprop="url" href="/person/1b278af064d677aca79f6ea61a9f17328/author/0"><span itemprop="name">T. Ehrhart</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pasquale Lisena" itemprop="url" href="/person/1b278af064d677aca79f6ea61a9f17328/author/1"><span itemprop="name">P. Lisena</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raphaël Troncy" itemprop="url" href="/person/1b278af064d677aca79f6ea61a9f17328/author/2"><span itemprop="name">R. Troncy</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 14:14:25 CET 2024KG Explorer: a Customisable Exploration Tool for Knowledge Graphsproceedings2021visualization knowledgegraph The growing adoption of Knowledge Graphs demands new applications which enable users to search and browse structured data in a suitable way depending on the domain. In this paper, we introduce KG Explorer, a web-based exploratory search engine for RDF-based Knowledge Graphs. The software can be configured in order to adapt to different information domains, customising both the UI components and the queries made for retrieving the information. It also includes features such as full-text search, facet-based advanced search, and the possibility to create lists of favourites items modelled in the knowledge graph.KG Explorer: a Customisable Exploration Tool for Knowledge Graphs - Archive ouverte HALhttps://puma.ub.uni-stuttgart.de/bibtex/2612910a6f41d62f6a4c443a42fd307f8/diglezakisdiglezakis2024-12-06T14:11:50+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leonidas Deligiannidis" itemprop="url" href="/person/1ea48f6c544ff8c4d2b328be0e9945eb5/author/0"><span itemprop="name">L. Deligiannidis</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Krys J. Kochut" itemprop="url" href="/person/1ea48f6c544ff8c4d2b328be0e9945eb5/author/1"><span itemprop="name">K. Kochut</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amit P. Sheth" itemprop="url" href="/person/1ea48f6c544ff8c4d2b328be0e9945eb5/author/2"><span itemprop="name">A. Sheth</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the ACM first workshop on CyberInfrastructure: information management in eScience</span>, </em></span><em>page <span itemprop="pagination">39–46</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">Association for Computing Machinery</span>, </em>(<em><span>Nov 9, 2007<meta content="Nov 9, 2007" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 14:11:50 CET 2024New York, NY, USAProceedings of the ACM first workshop on CyberInfrastructure: information management in eScience1139–46CIMS '07RDF data exploration and visualization2007visualization knowledgegraph 9We present Paged Graph Visualization (PGV), a new semi-autonomous tool for RDF data exploration and visualization. PGV consists of two main components: a) the "PGV explorer" and b) the "RDF pager" module utilizing BRAHMS, our high per-formance main-memory RDF storage system. Unlike existing graph visualization techniques which attempt to display the entire graph and then filter out irrelevant data, PGV begins with a small graph and provides the tools to incrementally explore and visualize relevant data of very large RDF ontologies. We implemented several techniques to visualize and explore hot spots in the graph, i.e. nodes with large numbers of immediate neighbors. In response to the user-controlled, semantics-driven direction of the exploration, the PGV explorer obtains the necessary sub-graphs from the RDF pager and enables their incremental visualization leaving the previously laid out sub-graphs intact. We outline the problem of visualizing large RDF data sets, discuss our interface and its implementation, and through a controlled experiment we show the benefits of PGV.RDF data exploration and visualization | Proceedings of the ACM first workshop on CyberInfrastructure: information management in eSciencehttps://puma.ub.uni-stuttgart.de/bibtex/28c4b4ac9448b97d34ebde2e83e4c7603/diglezakisdiglezakis2024-12-06T14:07:49+01:00visualization knowledgegraph <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chao-Wen Hsuan Yuan" itemprop="url" href="/person/1ff1606d5ec0424f68250df8bf50ce50f/author/0"><span itemprop="name">C. Hsuan Yuan</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tzu-Wei Yu" itemprop="url" href="/person/1ff1606d5ec0424f68250df8bf50ce50f/author/1"><span itemprop="name">T. Yu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jia-Yu Pan" itemprop="url" href="/person/1ff1606d5ec0424f68250df8bf50ce50f/author/2"><span itemprop="name">J. Pan</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wen-Chieh Lin" itemprop="url" href="/person/1ff1606d5ec0424f68250df8bf50ce50f/author/3"><span itemprop="name">W. Lin</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Transactions on Visualization and Computer Graphics</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">12</span>): <span itemprop="pagination">7702-7716</span></em> </span>(<em><span>December 2024<meta content="December 2024" itemprop="datePublished"/></span></em>)</span>Fri Dec 06 14:07:49 CET 2024IEEE Transactions on Visualization and Computer Graphics12127702-7716KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance302024visualization knowledgegraph Knowledge graphs have been commonly used to represent relationships between entities and are utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they did not consider various user needs and characteristics of knowledge graphs. Exploratory approaches specifically designed to uncover and summarize insights in knowledge graphs have not been well studied yet. In this article, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting the exploration of knowledge graphs with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and helping explore the entire network.KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance | IEEE Journals & Magazine | IEEE Xplorehttps://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> and <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>page <span itemprop="pagination">174-178</span>. </em>(<em><span>December 2020<meta content="December 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