Visually Connecting Historical Figures Through Event Knowledge Graphs

Latif, Shahid; Agarwal, Shivam; Gottschalk, Simon; Chrosch, Carina; Feit, Felix; Jahn, Johannes; Braun, Tobias; Tchenko, Yanick Christian; Demidova, Elena; Beck, Fabian

doi:10.1109/vis49827.2021.9623313

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Cashman et al's CAVA system allows users to interactively augment related attributes from knowledge graphs into an existing data set and visualize them during exploration and analysis tasks [33]. Similarly, Latif et al utilize EventKG in a visualization system to automatically integrate relevant event information and relationships for historical figures in an existing data set [34]. A goal that cuts across these examples is to help people explore differently structured data across independently located sources without interrupting the visual analytics process [35], [36].…”

Section: Systemsmentioning

confidence: 99%

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Coscia,

Suh,

Chang

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting approaches to the data preparation and analysis workflow: manual file-based ex-situ integration as a separate step from visual analytics operations; and automatic UI-based in-situ integration merged with visual analytics operations. Participants were asked to complete specific and free-form tasks with each interface, browsing for patterns, generating insights, and summarizing relationships between attributes distributed across multiple files. Analyzing participants' interactions and feedback, we found both task completion time and total interactions to be similar across interfaces and tasks, as well as unique integration strategies between interfaces and emergent behaviors related to satisficing and cognitive bias. Participants' time spent and interactions revealed that in-situ integration enabled users to spend more time on analysis tasks compared with ex-situ integration. Participants' integration strategies and analytical behaviors revealed differences in interface usage for generating and tracking hypotheses and insights. With these results, we synthesized preliminary guidelines for designing future visual analytics interfaces that can support integrating attributes throughout an active analysis process.

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Section: Systemsmentioning

confidence: 99%

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Coscia,

Suh,

Chang

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…HisVA focuses on an active learning process while interacting with the data, which is claimed to be more beneficial than traditional textbook-based learning. Apart from that, VisKonnect [15] is intended to represent characters involved in common events and comes up with a relationship graph as well as a chat interface that enables typing in a query and generates a short textual answer. It is to be noted that each of the mentioned tools is a 2D nonimmersive desktop application which comes along with a limited space and consequently might not be optimal for visualizations that should offer more complex relationships.…”

Section: Interactive Exploration Of Historical Data In 2dmentioning

confidence: 99%

Who Did What When? Discovering Complex Historical Interrelations in Immersive Virtual Reality

Derksen,

Becker,

Elahi

et al. 2023

2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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Figure 1: We present a tool for exploring historical data in VR using a head-mounted display. Left: The user is placed on a platform representing the main historical fragment (e.g., a person or an event) of their research (here Albert Einstein). Other related fragments are represented as colored labeled spheres and are arranged around the user based on their historical interrelationships. Users can select a sphere to make it the new main fragment, thereby allowing for an immersive exploration of the dataset. Right: For every sphere, a detail window with more information on the corresponding fragment can be opened. An exploration history in the lower field of view shows the order of visited main fragments (here Physics and Erwin Schr ödinger ). If a sphere is marked, a corresponding mark appears on a compass view in the upper field of view (e.g., a blue mark for Albert Einstein). The relation details, here shown for Albert Einstein, Austria-Hungary, and Max Planck, reveal additional details about their historical connections.

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“…In response to the user query, a resulting exploranative representation of data should then enable users to verify and validate presented facts and to explore related ones. For a specialized use case of exploring relationships among historical public figures, we have developed VisKonnect (Latif et al 2021a) together with project WorldKG (Chap. 1).…”

Section: A Chatbot Interface Providing Visual and Textual Answersmentioning

confidence: 99%

“…This chapter describes the results of the project vgiReports and summarizes as well as connects an excerpt of project-related publications (Latif et al 2021a(Latif et al ,b, 2022a and a preliminary work (Latif and Beck 2019a). Two of these works (Latif et al 2021a,b) report results from collaborations with other projects of the priority program.…”

Section: Introductionmentioning

confidence: 99%

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2022

Encyclopedia of Tourism Management and Marketing

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Visually Connecting Historical Figures Through Event Knowledge Graphs

Cited by 10 publications

References 18 publications

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Who Did What When? Discovering Complex Historical Interrelations in Immersive Virtual Reality

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