SemanticTours: A Conceptual Framework for Non-Linear, Knowledge Graph-Driven Data Tours
Daniel F\"urst, Matthijs Jansen op de Haar, Mennatallah El-Assady, Daniel A Keim, Maximilian T. Fischer

TL;DR
SemanticTours introduces a graph-based, non-linear approach to data exploration that enhances analytical reasoning in knowledge-centric domains like law, overcoming the limitations of traditional linear tours.
Contribution
It presents a novel semantic, graph-based model for interactive tours that allows non-linear navigation and hypothesis refinement in complex, knowledge-rich datasets.
Findings
Domain experts found graph-based tours better support reasoning.
The model effectively connects data via semantic relationships.
Evaluation demonstrates improved analytical support.
Abstract
Interactive tours help users explore datasets and provide onboarding. They rely on a linear sequence of views, showing a curated set of relevant data selections and introduce user interfaces. Existing frameworks of tours, however, often do not allow for branching and refining hypotheses outside of a rigid sequence, which is important in knowledge-centric domains such as law. For example, lawyers performing analytical case analysis need to iteratively weigh up different legal norms and construct strings of arguments. To address this gap, we propose SemanticTours, a semantic, graph-based model of tours that shifts from a sequence-based towards a graph-based navigation. Our model constructs a domain-specific knowledge graph that connects data elements based on user-definable semantic relationships. These relationships enable non-linear graph navigation that defines tours. We apply…
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Taxonomy
TopicsData Visualization and Analytics · Advanced Graph Neural Networks · Explainable Artificial Intelligence (XAI)
