mlsanityData to 7 Oct 2026

End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs

arXiv 2109.05817 ↗First posted 13 Sep 2021cs.CLcited by 11

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Abstract (arXiv)

Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leaving the important and non-trivial task of entity resolution (ER) outside the scope of E2E learning. In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime. Our approach is fully differentiable, thanks to its reliance on a recent method for building differentiable KGs (Cohen et al., 2020). We evaluate our E2E trained model on two public datasets and show that it comes close to baseline models that use hand-annotated entities.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

15 of 24 references.

References · 15

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  4. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  5. 2018Improving Text-to-SQL Evaluation Methodologycited by 133
  6. 2015Large-scale Simple Question Answering with Memory Networkscited by 127
  7. 2020Efficient One-Pass End-to-End Entity Linking for Questionscited by 76
  8. 2019Learning to Generalize from Sparse and Underspecified Rewardscited by 39
  9. 2020Scalable Neural Methods for Reasoning With a Symbolic Knowledge Basecited by 32
  10. 2018SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approachcited by 18
  11. 2018Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categoriescited by 9
  12. 2020Less is More: Data-Efficient Complex Question Answering over Knowledge Basescited by 9
3 more
  1. 2019Learning Representation Mapping for Relation Detection in Knowledge Base Question Answeringcited by 7
  2. 2017Entity Linking for Queries by Searching Wikipedia Sentencescited by 5
  3. 2018No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graphcited by 3

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Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 3.

11 papers.

  1. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  2. 2022Retrieval Augmented Visual Question Answering with Outside Knowledgecited by 93
  3. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 71
  4. 2023Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answeringcited by 64
  5. 2023Knowledge-Augmented Language Model Verificationcited by 21
  6. 2023An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answeringcited by 19
  7. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  8. 2025Knowledge Graph-extended Retrieval Augmented Generation for Question Answeringcited by 14
  9. 2023Large Language Models Meet Knowledge Graphs to Answer Factoid Questionscited by 11
  10. 2025BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answeringcited by 10
  11. 2024SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decodingcited by 5