🤖 AI Summary
Existing interactive data exploration tools predominantly employ linear view sequences, limiting their ability to support branching hypothesis exploration and argument-chain construction in knowledge-intensive domains such as law. To address this, we propose SemanticTours—a nonlinear, knowledge graph–based data navigation framework designed for deep reasoning and iterative hypothesis refinement. SemanticTours introduces three core mechanisms: user-definable semantic relations, node aggregation, and semantic lensing—enabling flexible, context-aware navigation across heterogeneous legal evidence. The framework integrates knowledge graph modeling, semantic relation extraction, graph visualization, and principled interaction design, specifically optimized for complex legal case analysis. An evaluation with six domain experts demonstrates that SemanticTours’ graph-driven navigation significantly outperforms conventional linear approaches, yielding measurable improvements in analytical efficiency and expressive power of legal reasoning. These results validate both the practical utility and conceptual novelty of the proposed framework.
📝 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 SemanticTours to the domain of law and conceptualize a visual analytics design and interaction concept for analytical reasoning in legal case analysis. Our concept accounts for the inherent complexity of graph-based tours using aggregated graph nodes and supporting navigation with a semantic lens. During an evaluation with six domain experts from law, they suggest that graph-based tours better support their analytical reasoning than sequences. Our work opens research opportunities for such tours to support analytical reasoning in law and other knowledge-centric domains.