# A Universal Question-Answering Platform for Knowledge Graphs

arXiv: https://arxiv.org/abs/2303.00595 · HTML: https://mlsanity.com/paper/2303.00595/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Logical Neural Networks](https://mlsanity.com/paper/2006.13155/) (2020)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [Message Passing for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1908.06917/) (2019)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Generative Relation Linking for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.07337/) (2021)
- [Natural Language Interfaces to Data](https://mlsanity.com/paper/2212.13074/) (2022)
- [A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs](https://mlsanity.com/paper/2102.13027/) (2021)
- [Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data](https://mlsanity.com/paper/2104.13744/) (2021)
- [A Survey of RDF Data Management Systems](https://mlsanity.com/paper/1601.00707/) (2016)
- [A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases](https://mlsanity.com/paper/1903.12356/) (2019)

## Built on by (most cited first, 17 of 17)

- [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://mlsanity.com/paper/2311.07914/) (2023)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering](https://mlsanity.com/paper/2402.14320/) (2024)
- [TrustUQA: A Trustful Framework for Unified Structured Data Question Answering](https://mlsanity.com/paper/2406.18916/) (2024)
- [Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs](https://mlsanity.com/paper/2501.09928/) (2025)
- [SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding](https://mlsanity.com/paper/2407.01626/) (2024)
- [Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2511.20940/) (2025)
- [Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation](https://mlsanity.com/paper/2408.13432/) (2024)
- [InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations](https://mlsanity.com/paper/2511.02002/) (2025)
- [Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory](https://mlsanity.com/paper/2605.26252/) (2026)
- [Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System](https://mlsanity.com/paper/2411.03906/) (2024)
- [Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion](https://mlsanity.com/paper/2506.23137/) (2025)
- [ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources](https://mlsanity.com/paper/2308.04800/) (2023)
- [Beyond Prefixes: Graph-as-Memory Cross-Attention for Knowledge Graph Completion with Large Language Models](https://mlsanity.com/paper/2510.08966/) (2025)
- [Comprehending Spatio-temporal Data via Cinematic Storytelling using Large Language Models](https://mlsanity.com/paper/2510.17301/) (2025)
- [Towards Researcher Agents for Knowledge-Graph Question Answering](https://mlsanity.com/paper/2608.07700/) (2026)
- [What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework](https://mlsanity.com/paper/2609.12746/) (2026)

## Abstract (arXiv)

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.