# Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning

arXiv: https://arxiv.org/abs/2110.12679 · HTML: https://mlsanity.com/paper/2110.12679/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 77 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [Learning by Abstraction: The Neural State Machine](https://mlsanity.com/paper/1907.03950/) (2019)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges](https://mlsanity.com/paper/2007.13069/) (2020)
- [Improved Neural Relation Detection for Knowledge Base Question Answering](https://mlsanity.com/paper/1704.06194/) (2017)
- [UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering](https://mlsanity.com/paper/1904.01246/) (2019)
- [SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach](https://mlsanity.com/paper/1804.08798/) (2018)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Meaningful Answer Generation of E-Commerce Question-Answering](https://mlsanity.com/paper/2011.07307/) (2020)
- [Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts](https://mlsanity.com/paper/1912.01858/) (2019)
- [CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector](https://mlsanity.com/paper/2110.12364/) (2021)

## Built on by (most cited first, 7 of 7)

- [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://mlsanity.com/paper/2305.17104/) (2023)
- [ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification](https://mlsanity.com/paper/2305.15024/) (2023)
- [FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs](https://mlsanity.com/paper/2608.24846/) (2026)
- [A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights](https://mlsanity.com/paper/2409.12853/) (2024)
- [GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2312.02317/) (2023)
- [Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering](https://mlsanity.com/paper/2603.01853/) (2026)
- [FedV-KGQA in Practice: Design Lessons and an Interactive Prototype](https://mlsanity.com/paper/2609.13661/) (2026)

## Abstract (arXiv)

Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the multi-hop relational chain preserved in KG to arrive at the right answer. Despite recent successes, the existing works on answering multi-hop complex questions still face the following challenges: i) The absence of an explicit relational chain order reflected in user-question stems from a misunderstanding of a user's intentions. ii) Incorrectly capturing relational types on weak supervision of which dataset lacks intermediate reasoning chain annotations due to expensive labeling cost. iii) Failing to consider implicit relations between the topic entity and the answer implied in structured KG because of limited neighborhoods size constraint in subgraph retrieval-based algorithms.To address these issues in multi-hop KGQA, we propose a novel model herein, namely Relational Chain based Embedded KGQA (Rce-KGQA), which simultaneously utilizes the explicit relational chain revealed in natural language question and the implicit relational chain stored in structured KG. Our extensive empirical study on three open-domain benchmarks proves that our method significantly outperforms the state-of-the-art counterparts like GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify the effectiveness of our method on the multi-hop KGQA task. We have made our model's source code available at github: https://github.com/albert-jin/Rce-KGQA.