Knowledge Graphs versus Hierarchies

Sarrafzadeh, Bahareh; Vtyurina, Alexandra; Lank, Edward; Vechtomova, Olga

doi:10.1145/2854946.2854958

Cited by 13 publications

(2 citation statements)

References 27 publications

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“…The natural alternative to a hierarchical information structure is a graph structure. However, it has been shown that hierarchical structures can better support overview and comprehension, especially for readers with low prior knowledge of the content [ATM09,SVLV16]. In contrast, graph structures are better for certain information seeking tasks for readers with higher prior knowledge, but this structure imposes a more demanding process on the reader [ATM09,SVLV16].…”

Section: Dynamic Insight Hierarchiesmentioning

confidence: 99%

InsideInsights: Integrating Data‐Driven Reporting in Collaborative Visual Analytics

Mathisen

Horak

Klokmose

et al. 2019

Computer Graphics Forum

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Analyzing complex data is a non‐linear process that alternates between identifying discrete facts and developing overall assessments and conclusions. In addition, data analysis rarely occurs in solitude; multiple collaborators can be engaged in the same analysis, or intermediate results can be reported to stakeholders. However, current data‐driven communication tools are detached from the analysis process and promote linear stories that forego the hierarchical and branching nature of data analysis, which leads to either too much or too little detail in the final report. We propose a conceptual design for integrated data‐driven reporting that allows for iterative structuring of insights into hierarchies linked to analytic provenance and chosen analysis views. The hierarchies become dynamic and interactive reports where collaborators can review and modify the analysis at a desired level of detail. Our web‐based InsideInsights system provides interaction techniques to annotate states of analytic components, structure annotations, and link them to appropriate presentation views. We demonstrate the generality and usefulness of our system with two use cases and a qualitative expert review.

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Section: Dynamic Insight Hierarchiesmentioning

confidence: 99%

InsideInsights: Integrating Data‐Driven Reporting in Collaborative Visual Analytics

Mathisen

Horak

Klokmose

et al. 2019

Computer Graphics Forum

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“…However, this research, based on a convenience sample, is often addressing only a limited number of relationships, usually pertaining to co-authorship and shared research topics. Our approach aggregates different web resources (including multiple sources indicating researchers' expertise) in a single knowledge graph-since research has shown that the graph-based interface is more practical for finding specific information and simple question-answering tasks, compared to hierarchically organized information [25]. Furthermore, we expand the scope of relationships to represent the interdisciplinary domain, as three dimensions are considered as a minimum to understand the full complexity of social structures [26].…”

Section: Network-based Representation and Discoverymentioning

confidence: 99%

Community Design of a Knowledge Graph to Support Interdisciplinary PhD Students

Gardasevic

Gazan

2023

Lecture Notes in Computer Science

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How do PhD students discover the resources and relationships conducive to satisfaction and success in their degree programs? This study proposes a community-grounded, extensible knowledge graph to make explicit and tacit information intuitively discoverable, by capturing and visualizing relationships between people based on their activities and relations to information resources in a particular domain. Students in an interdisciplinary PhD program were engaged through three workshops to provide insights into the dynamics of interactions with others and relevant data categories to be included in the graph data model. Based on these insights we propose a model, serving as a testbed for exploring multiplex graph visualizations and a potential basis of the information system to facilitate information discovery and decision-making. We discovered that some of the tacit knowledge can be explicitly encoded, while the rest of it must stay within the community. The graph-based visualization of the social and knowledge networks can serve as a pointer toward the people having the relevant information, one can reach out to, online or in person.

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