Answering Questions Over Knowledge Graphs Using Logic Programming Along with Language Models

Question Answering over Knowledge Graphs (KGQA) is the task of answering natural language questions over a knowledge graph (KG). This task requires a model to reason over multiple edges of the KG to reach the right answer. In this work, we present a method to equip large language models (LLMs) with classic logical programming languages to provide an explainable solution to the problem. Our goal is to extract the representation of the question in the form of a Prolog query, which can then be used to answer the query programmatically. To demonstrate the effectiveness of this approach, we use the MetaQA dataset and show that our method finds the correct answer entities for all the questions in the test dataset.