Ripple Down Rules for Question Answering

TL;DR

This paper presents KbQAS, an ontology-based question answering system for Vietnamese that uses a systematic question analysis approach to convert questions into intermediate representations, achieving promising accuracy and adaptability.

Contribution

Introduces KbQAS, the first Vietnamese ontology-based question answering system, with a novel question analysis method that simplifies adaptation to new domains and languages.

Findings

Achieved 84.1% accuracy in question analysis

Achieved 82.4% accuracy in answer retrieval

System is adaptable to new domains and languages

Abstract

Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.