EAGER: Explainable question answering using knowledge graphs

Andrew Chai, Alireza Vezvaei, Lukasz Golab, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta, Morteza Zihayat
January 2023

Abstract

We present EAGER: a tool for answering questions expressed in natural language. Core to EAGER is a modular pipeline for generating a knowledge graph from raw text without human intervention. Notably, EAGER uses the knowledge graph to answer questions and to explain the reasoning behind the derivation of answers. Our demonstration will showcase both the automated knowledge graph generation pipeline and the explainable question answering functionality. Lastly, we outline open problems and directions for future work.

Type
Conference paper
Publication
Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

This work introduces explainable question answering using knowledge graphs.

Explainable AI Question Answering Knowledge Graphs Interpretability Graph Data Management
Morteza Zihayat
Morteza Zihayat
Principal Investigator

Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.