Complex Knowledge Base Question Answering: A Survey

TL;DR

This survey reviews recent advances in complex knowledge base question answering (KBQA), focusing on methods to handle questions with multiple subjects, compound relations, or numerical operations, highlighting challenges and future directions.

Contribution

It provides a comprehensive overview of recent methods, datasets, and challenges in complex KBQA, emphasizing the distinction between semantic parsing and information retrieval approaches.

Findings

Semantic parsing and IR-based methods are main approaches.

Challenges include question complexity and dataset limitations.

Future directions involve improved reasoning and dataset development.

Abstract

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research.