Available at: https://digitalcommons.calpoly.edu/theses/2885
Date of Award
6-2024
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Lubomir Stanchev
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Evaluating active Question Answering (QA) systems, as users ask questions outside of the original testing data, has proven to be difficult, due to the difficulty of gauging answer quality without ground truth responses. We propose KGScore-Open, a configurable system capable of scoring questions and answers in Open Domain Question Answering (Open-QA) without ground truth answers present by leveraging DBPedia, a Knowledge Graph (KG) derived from Wikipedia, to score question-answer pairs. The system maps entities from questions and answers to DBPedia nodes, constructs a Knowledge Graph based on these entities, and calculates a relatedness score. Our system is validated on multiple datasets, achieving up to 83% accuracy in differentiating relevant from irrelevant answers in the Natural Questions dataset, 55% accuracy in classifying correct versus incorrect answers (hallucinations) in the TruthfulQA and HaluEval datasets, and 54% accuracy on the QA-Eval task using the EVOUNA dataset. The contributions of this work include a novel scoring system for indicating both relevancy and answer confidence in Open-QA without the need for ground truth answers, demonstrated efficacy across various tasks, and an extendable framework applicable to different KGs for evaluating QA systems of other domains.