PUMA publications for /group/researchcode/visualizationFri 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.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.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.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.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.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.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.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.Fri Sep 22 15:44:04 CEST 2023DATA PROVENANCE VISUALIZATION METHODOLOGIES2023provenance metadata visualization Tue Nov 27 11:50:43 CET 2018TaPPProvenance map orbiter: Interactive exploration of large provenance graphs.2011provenance metadata visualization tools Tue Nov 27 11:46:18 CET 2018Procedia Computer ScienceInternational Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland1592 - 1601A Framework for Provenance Analysis and Visualization1082017provenance visualization Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.Tue Nov 27 10:10:51 CET 2018New York, NY, USAProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems2437--2446CHI '15Provenance for the People: An HCI Perspective on the W3C PROV Standard Through an Online Game2015game provenance visualization human-computer-interaction Mon Nov 26 15:17:23 CET 20189th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)Proceedings of the 9th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)06Visualizing Provenance using Comics2017provenance metadata visualization comics Understanding how a piece of data was produced, where it was stored, and by whom it was accessed, is crucial information in many processes. To understand the trace of data, the provenance of that data can be recorded and analyzed. But it is sometimes hard to understand this provenance information, especially for people who are not familiar with software or computer science. To close this gap, we present a visualization technique for data provenance using comics strips. Each strip of the comic represents an activity of the provenance graph, for example, using an app, storing or retrieving data on a cloud service, or generating a diagram. The comic strips are generated automatically using recorded provenance graphs. These provenance comics are intended to enable people to understand the provenance of their data and realize crucial points more easily.Mon Nov 26 15:13:30 CET 2018Computers021Quantified Self and Personal InformaticsVisualizing the Provenance of Personal Data Using Comics72018provenance metadata visualization comics Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comic strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.Mon Nov 19 09:03:25 CET 2018A User Guide to TwoRavens: An overview of features and capabilities2016forschungsdaten visualization tools statistics Mon Nov 19 08:40:13 CET 2018HT (Doctoral Consortium / Late-breaking Results / Workshops)conf/ht/2014dcCEUR Workshop ProceedingsStatistical Modeling by Gesture: A graphical, Browser-based Statistical Interface for Data Repositories.12102014forschungsdaten visualization data tools dataverse statistics Fri May 11 11:08:59 CEST 2018ChamProvenance and Annotation of Data and Processes71--82Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data2016forschungsdaten provenance metadata visualization The analysis of provenance data for an experiment is often crucial to understand the achieved results. For long-running experiments or when provenance is captured at a low granularity, this analysis process can be overwhelming to the user due to the large volume of provenance data. In this paper we introduce, Prov Viewer, a provenance visualization tool that enables users to interactively explore provenance data. Among the visualization and exploratory features, we can cite zooming, filtering, and coloring. Moreover, we use of other properties such as shape and size to distinguish visual elements. These exploratory features are linked to the provenance semantics to ease the comprehension process. We also introduce collapsing and filtering strategies, allowing different levels of granularity exploration and analysis. We describe case studies that show how Prov Viewer has been successfully used to explore provenance in different domains, including games and urban data.