AdaptLIL: A Gaze-Adaptive Visualization for Ontology Mapping

TL;DR

AdaptLIL is a real-time, gaze-adaptive visualization tool for ontology mapping that personalizes graphical overlays based on users' eye gaze, integrating deep learning and web tech.

Contribution

It introduces a novel gaze-based adaptive visualization system for ontology mapping, combining real-time eye tracking with deep learning for personalized visualizations.

Findings

Personalized overlays improve user interaction.

Real-time adaptation based on eye gaze is feasible.

System integrates multimodal data for ontology visualization.

Abstract

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.