# An Online Question Answering System based on Sub-graph Searching

arXiv: https://arxiv.org/abs/2107.13684 · HTML: https://mlsanity.com/paper/2107.13684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce](https://mlsanity.com/paper/2004.10919/) (2020)

## Abstract (arXiv)

Knowledge graphs (KGs) have been widely used for question answering (QA) applications, especially the entity based QA. However, searching an-swers from an entire large-scale knowledge graph is very time-consuming and it is hard to meet the speed need of real online QA systems. In this pa-per, we design a sub-graph searching mechanism to solve this problem by creating sub-graph index, and each answer generation step is restricted in the sub-graph level. We use this mechanism into a real online QA chat system, and it can bring obvious improvement on question coverage by well answer-ing entity based questions, and it can be with a very high speed, which en-sures the user experience of online QA.