# Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection

arXiv: https://arxiv.org/abs/2109.05808 · HTML: https://mlsanity.com/paper/2109.05808/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [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)
- [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)
- [Improving Text-to-SQL Evaluation Methodology](https://mlsanity.com/paper/1806.09029/) (2018)
- [TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph](https://mlsanity.com/paper/2104.07302/) (2021)
- [Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base](https://mlsanity.com/paper/2002.06115/) (2020)
- [Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing](https://mlsanity.com/paper/1908.09940/) (2019)
- [Neural Query Language: A Knowledge Base Query Language for Tensorflow](https://mlsanity.com/paper/1905.06209/) (2019)

## Built on by (most cited first, 12 of 12)

- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://mlsanity.com/paper/2306.04136/) (2023)
- [Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering](https://mlsanity.com/paper/2210.01613/) (2022)
- [ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models](https://mlsanity.com/paper/2310.08975/) (2023)
- [SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions](https://mlsanity.com/paper/2407.11417/) (2024)
- [ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.13650/) (2022)
- [NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA](https://mlsanity.com/paper/2310.15484/) (2023)
- [KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering](https://mlsanity.com/paper/2509.04716/) (2025)
- [S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering](https://mlsanity.com/paper/2603.23512/) (2026)
- [Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata](https://mlsanity.com/paper/2305.14202/) (2023)
- [Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2312.11922/) (2023)
- [KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04093/) (2025)

## Abstract (arXiv)

End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al., 2020) have focused on single-entity questions using a relation following operation. In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. We find that introducing intersection improves performance over a baseline model on two datasets, WebQuestionsSP (69.6% to 73.3% Hits@1) and ComplexWebQuestions (39.8% to 48.7% Hits@1), and in particular, improves performance on questions with multiple entities by over 14% on WebQuestionsSP and by 19% on ComplexWebQuestions.