# KBQA: Learning Question Answering over QA Corpora and Knowledge Bases

## TL;DR

This paper introduces KBQA, a question answering system that uses learned templates over large knowledge bases and QA corpora to understand and answer a wide range of questions more effectively and efficiently.

## Contribution

The paper proposes a novel template-based question representation and expands knowledge base predicates, significantly improving coverage and performance in QA tasks.

## Key findings

- Learned 27 million templates for 2782 intents.
- Expanded predicates in RDF knowledge base by 57 times.
- Outperformed state-of-the-art systems on QALD benchmarks.

## Abstract

Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of "canned" questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates, over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city's population, we learn templates such as What's the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.