# Knowledge Graph Question Answering using Graph-Pattern Isomorphism

arXiv: https://arxiv.org/abs/2103.06752 · HTML: https://mlsanity.com/paper/2103.06752/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [Message Passing for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1908.06917/) (2019)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1907.09361/) (2019)
- [Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering](https://mlsanity.com/paper/1908.11053/) (2019)
- [Towards a Question Answering System over the Semantic Web](https://mlsanity.com/paper/1803.00832/) (2018)
- [Neural Machine Translation for Query Construction and Composition](https://mlsanity.com/paper/1806.10478/) (2018)
- [MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach](https://mlsanity.com/paper/1707.05288/) (2017)
- [Question Answering over Knowledge Graphs via Structural Query Patterns](https://mlsanity.com/paper/1910.09760/) (2019)
- [Template-based Question Answering using Recursive Neural Networks](https://mlsanity.com/paper/2004.13843/) (2020)
- [Entity Linking in 40 Languages using MAG](https://mlsanity.com/paper/1805.11467/) (2018)
- [Learning to Order Sub-questions for Complex Question Answering](https://mlsanity.com/paper/1911.04065/) (2019)

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

- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?](https://mlsanity.com/paper/2205.06573/) (2022)
- [MST5 -- Multilingual Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2407.06041/) (2024)
- [A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation](https://mlsanity.com/paper/2211.10271/) (2022)
- [A Comparative Study of Question Answering over Knowledge Bases](https://mlsanity.com/paper/2211.08170/) (2022)
- [Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering](https://mlsanity.com/paper/2503.01385/) (2025)
- [Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System](https://mlsanity.com/paper/2411.03906/) (2024)
- [Explainable Benchmarking through the Lense of Concept Learning](https://mlsanity.com/paper/2510.20439/) (2025)

## Abstract (arXiv)

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.