Visual support designed to facilitate human interaction with ontological data has largely focused on one-size-fits-all solutions, where less attention has been paid to providing personalized visual cues to assist the user in the process of comprehending complex datasets and relationships. To address this research gap, this paper presents an adaptive visualization designed to tailor visual cues to an individual user during ontology mapping activities. The adaptative visualization utilizes physiological signals such as eye gaze to predict one’s success in a given task, and in the event of a predicted failure, real-time visual interventions in the form of highlighting and deemphasis are deployed to direct user attention and assist with task completion. The proposed adaptive visualization is compared to a non-adaptive baseline in a user study involving 76 participants. The experimental results show statistically significant increases in user success with similarly perceived workload and time on task compared to those of the baseline, indicating that the proposed adaptive visualization is effective at improving user performance without tradeoff in workload or task speed. Furthermore, we report on the impact of highlighting and deemphasis on user success and provide recommendations in the development of future adaptive visualizations in human-machine teaming scenarios.