AdaptLIL: A Gaze-Adaptive Visualization for Ontology Mapping
This addresses the problem of personalized ontology mapping visualization for users, but it appears incremental as it adapts existing visualization techniques with gaze input.
The paper tackled the problem of visualizing ontology mappings by introducing AdaptLIL, a gaze-adaptive system that tailors link-indented list visualizations in real-time based on user eye gaze, resulting in a method that uniquely curtails graphical overlays for individual users.
This paper showcases AdaptLIL, a real-time adaptive link-indented list ontology mapping visualization that uses eye gaze as the primary input source. Through a multimodal combination of real-time systems, deep learning, and web development applications, this system uniquely curtails graphical overlays (adaptations) to pairwise mappings of link-indented list ontology visualizations for individual users based solely on their eye gaze.