Explanation Container in Case-Based Biomedical Question-Answering

TL;DR

This paper introduces the Explanation Container within a case-based agent for biomedical question-answering, enhancing transparency by providing explanations for ranked results in a multi-agent biomedical data system.

Contribution

It presents the design of a novel Explanation Container for a case-based agent, improving explainability in biomedical question-answering systems.

Findings

The Explanation Container effectively provides case-based explanations.

The system improves transparency in biomedical data retrieval.

The architecture supports multi-agent integration for complex queries.

Abstract

The National Center for Advancing Translational Sciences(NCATS) Biomedical Data Translator (Translator) aims to attenuate problems faced by translational scientists. Translator is a multi-agent architecture consisting of six autonomous relay agents (ARAs) and eight knowledge providers (KPs). In this paper, we present the design of the Explanatory Agent (xARA), a case-based ARA that answers biomedical queries by accessing multiple KPs, ranking results, and explaining the ranking of results. The Explanatory Agent is designed with five knowledge containers that include the four original knowledge containers and one additional container for explanation - the Explanation Container. The Explanation Container is case-based and designed with its own knowledge containers.