# Modern Baselines for SPARQL Semantic Parsing

arXiv: https://arxiv.org/abs/2204.12793 · HTML: https://mlsanity.com/paper/2204.12793/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Measuring Compositional Generalization: A Comprehensive Method on Realistic Data](https://mlsanity.com/paper/1912.09713/) (2019)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?](https://mlsanity.com/paper/2010.12725/) (2020)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures](https://mlsanity.com/paper/2007.08970/) (2020)
- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2001.11458/) (2020)
- [Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations](https://mlsanity.com/paper/2104.07478/) (2021)
- [SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases](https://mlsanity.com/paper/2003.13956/) (2020)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2109.03614/) (2021)
- [Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering](https://mlsanity.com/paper/1908.11053/) (2019)
- [Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis](https://mlsanity.com/paper/2201.08174/) (2022)
- [SPARQL as a Foreign Language](https://mlsanity.com/paper/1708.07624/) (2017)
- [Knowledge Graph Question Answering using Graph-Pattern Isomorphism](https://mlsanity.com/paper/2103.06752/) (2021)
- [Template-based Question Answering using Recursive Neural Networks](https://mlsanity.com/paper/2004.13843/) (2020)
- [A Chinese Multi-type Complex Questions Answering Dataset over Wikidata](https://mlsanity.com/paper/2111.06086/) (2021)

## Built on by (most cited first, 30 of 34)

- [Calibrated Interpretation: Confidence Estimation in Semantic Parsing](https://mlsanity.com/paper/2211.07443/) (2022)
- [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022)
- [DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2303.13351/) (2023)
- [CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era](https://mlsanity.com/paper/2412.18702/) (2024)
- [Assessing SPARQL capabilities of Large Language Models](https://mlsanity.com/paper/2409.05925/) (2024)
- [Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance](https://mlsanity.com/paper/2405.17076/) (2024)
- [AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots](https://mlsanity.com/paper/2508.02999/) (2025)
- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [NLQxform: A Language Model-based Question to SPARQL Transformer](https://mlsanity.com/paper/2311.07588/) (2023)
- [GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering](https://mlsanity.com/paper/2303.13284/) (2023)
- [SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding](https://mlsanity.com/paper/2407.01626/) (2024)
- [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)
- [MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection](https://mlsanity.com/paper/2405.19285/) (2024)
- [Dynamic Few-Shot Learning for Knowledge Graph Question Answering](https://mlsanity.com/paper/2407.01409/) (2024)
- [Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems](https://mlsanity.com/paper/2309.16248/) (2023)
- [Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval](https://mlsanity.com/paper/2502.13369/) (2025)
- [Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language](https://mlsanity.com/paper/2505.19971/) (2025)
- [Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark](https://mlsanity.com/paper/2603.06582/) (2026)
- [Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation](https://mlsanity.com/paper/2408.13432/) (2024)
- [FRASE: Structured Representations for Generalizable SPARQL Query Generation](https://mlsanity.com/paper/2503.22144/) (2025)
- [The Wikidata Query Logs Dataset](https://mlsanity.com/paper/2602.14594/) (2026)
- [The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing](https://mlsanity.com/paper/2305.15108/) (2023)
- [Automating SPARQL Query Translations between DBpedia and Wikidata](https://mlsanity.com/paper/2507.10045/) (2025)
- [The benefits of query-based KGQA systems for complex and temporal questions in LLM era](https://mlsanity.com/paper/2507.11954/) (2025)
- [From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2508.01815/) (2025)
- [GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons](https://mlsanity.com/paper/2604.21133/) (2026)
- [Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP](https://mlsanity.com/paper/2605.20066/) (2026)
- [HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities](https://mlsanity.com/paper/2605.09348/) (2026)
- [DeSQ: Decomposition-based SPARQL Query Generation](https://mlsanity.com/paper/2606.00203/) (2026)

## Abstract (arXiv)

In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.