# Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation

arXiv: https://arxiv.org/abs/2502.12737 · HTML: https://mlsanity.com/paper/2502.12737/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2212.00959/) (2022)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2210.00063/) (2022)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases](https://mlsanity.com/paper/2210.12925/) (2022)
- [FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering](https://mlsanity.com/paper/2405.13873/) (2024)
- [Program Transfer for Answering Complex Questions over Knowledge Bases](https://mlsanity.com/paper/2110.05743/) (2021)
- [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022)
- [Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval](https://mlsanity.com/paper/2402.02175/) (2024)
- [LLM-based Discriminative Reasoning for Knowledge Graph Question Answering](https://mlsanity.com/paper/2412.12643/) (2024)

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

- [Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning](https://mlsanity.com/paper/2509.26383/) (2025)
- [Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking](https://mlsanity.com/paper/2603.00267/) (2026)
- [Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning](https://mlsanity.com/paper/2508.17905/) (2025)
- [R2-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation](https://mlsanity.com/paper/2508.01475/) (2025)
- [Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement](https://mlsanity.com/paper/2604.14749/) (2026)
- [DeSQ: Decomposition-based SPARQL Query Generation](https://mlsanity.com/paper/2606.00203/) (2026)
- [KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems](https://mlsanity.com/paper/2606.27742/) (2026)
- [AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2608.14228/) (2026)

## Abstract (arXiv)

Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements at test time,we introduce SG-KBQA: a novel model that injects schema contexts into entity retrieval and logical form generation to tackle this issue. It uses the richer semantics and awareness of the knowledge base structure provided by schema contexts to enhance generalizability. We show that SG-KBQA achieves strong generalizability, outperforming state-of-the-art models on two commonly used benchmark datasets across a variety of test settings. Our source code is available at https://github.com/gaosx2000/SG_KBQA.