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Updated: Jan 30, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Abstract:
Our knowledge of events and situations in the world plays a critical role in our ability to understand what is happening around us, to predict what might happen next, and to comprehend language. What has not been so clear is the form and structure of this knowledge, how it is learned, and how it is deployed in real time. Despite many important theoretical proposals, often using different terminology such as schemas, scripts, frames, and event knowledge, developing a model that addresses these three questions (the form, learning, and use of such knowledge) has remained an elusive challenge for decades. In this article, we present a connectionist model of event knowledge that attempts to fill this gap. From sequences of activities, the model learns both the internal structure of activities as well as the temporal structure that organizes activity sequences. The model simulates a wide range of human behaviors that have been argued to involve the use of event knowledge and the temporal structure of events. Furthermore, it makes testable predictions about behaviors not yet observed. Most importantly, the model's ability to learn event structure from experience is a novel solution to the question, "What is the form and representation of event knowledge?" (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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