# Simple Question Answering with Subgraph Ranking and Joint-Scoring

arXiv: https://arxiv.org/abs/1904.04049 · HTML: https://mlsanity.com/paper/1904.04049/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph](https://mlsanity.com/paper/2104.07302/) (2021)
- [An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering](https://mlsanity.com/paper/2303.10368/) (2023)
- [Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning](https://mlsanity.com/paper/2111.01415/) (2021)
- [Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering](https://mlsanity.com/paper/2209.03005/) (2022)
- [Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2312.11922/) (2023)
- [OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data](https://mlsanity.com/paper/2112.15356/) (2021)
- [Better Query Graph Selection for Knowledge Base Question Answering](https://mlsanity.com/paper/2204.12662/) (2022)
- [A Method of Query Graph Reranking for Knowledge Base Question Answering](https://mlsanity.com/paper/2204.12808/) (2022)

## Abstract (arXiv)

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject--relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.