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

arXiv: https://arxiv.org/abs/1903.02419 · HTML: https://mlsanity.com/paper/1903.02419/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 30 of 44)

- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Exploring Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2308.13916/) (2023)
- [Combining Feature and Instance Attribution to Detect Artifacts](https://mlsanity.com/paper/2107.00323/) (2021)
- [TWEAC: Transformer with Extendable QA Agent Classifiers](https://mlsanity.com/paper/2104.07081/) (2021)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering](https://mlsanity.com/paper/2311.06503/) (2023)
- [Multi-modal Entity Alignment in Hyperbolic Space](https://mlsanity.com/paper/2106.03619/) (2021)
- [LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments](https://mlsanity.com/paper/2408.15903/) (2024)
- [Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2109.03614/) (2021)
- [Learning to Walk with Dual Agents for Knowledge Graph Reasoning](https://mlsanity.com/paper/2112.12876/) (2021)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models](https://mlsanity.com/paper/2310.01290/) (2023)
- [Large Language Models Meet Knowledge Graphs to Answer Factoid Questions](https://mlsanity.com/paper/2310.02166/) (2023)
- [QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion](https://mlsanity.com/paper/2105.09002/) (2021)
- [Entity Retrieval for Answering Entity-Centric Questions](https://mlsanity.com/paper/2408.02795/) (2024)
- [Knowledge Graph Question Answering using Graph-Pattern Isomorphism](https://mlsanity.com/paper/2103.06752/) (2021)
- [From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanation](https://mlsanity.com/paper/2109.04171/) (2021)
- [Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base](https://mlsanity.com/paper/2207.03680/) (2022)
- [Interactive Contrastive Learning for Self-supervised Entity Alignment](https://mlsanity.com/paper/2201.06225/) (2022)
- [Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question](https://mlsanity.com/paper/2201.01209/) (2022)
- [Triple Classification for Scholarly Knowledge Graph Completion](https://mlsanity.com/paper/2111.11845/) (2021)
- [Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes](https://mlsanity.com/paper/2108.00513/) (2021)
- [Modeling Global Semantics for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2101.01510/) (2021)
- [CBench: Towards Better Evaluation of Question Answering Over Knowledge Graphs](https://mlsanity.com/paper/2105.00811/) (2021)
- [On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions](https://mlsanity.com/paper/2406.10885/) (2024)
- [Discovering Fine-Grained Semantics in Knowledge Graph Relations](https://mlsanity.com/paper/2202.08917/) (2022)
- [CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm](https://mlsanity.com/paper/2210.06246/) (2022)
- [MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base](https://mlsanity.com/paper/2212.05254/) (2022)
- [COMBO: A Complete Benchmark for Open KG Canonicalization](https://mlsanity.com/paper/2302.03905/) (2023)

## Abstract (arXiv)

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.