# Querying Knowledge via Multi-Hop English Questions

## TL;DR

This paper introduces KALM-QA, an extension of the KALM system, capable of answering complex English questions with perfect accuracy on a large movie-related dataset, outperforming machine learning methods.

## Contribution

The paper presents KALM-QA, a knowledge-based question answering system that achieves 100% accuracy on a large-scale dataset, demonstrating the effectiveness of natural language knowledge querying.

## Key findings

- KALM-QA achieves 100% accuracy on MetaQA.
- KALM-QA outperforms existing machine learning approaches.
- Supports complex English questions in knowledge querying.

## Abstract

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.