Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions
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Abstract (arXiv)
When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We create GrailQAbility, a new benchmark KBQA dataset with unanswerability, by first identifying various forms of KB incompleteness that make questions unanswerable, and then systematically adapting GrailQA (a popular KBQA dataset with only answerable questions). Experimenting with three state-of-the-art KBQA models, we find that all three models suffer a drop in performance even after suitable adaptation for unanswerable questions. In addition, these often detect unanswerability for wrong reasons and find specific forms of unanswerability particularly difficult to handle. This underscores the need for further research in making KBQA systems robust to unanswerability
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 20 Dec 2022.
Builds on
22 of 39 references.
References · 22
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- 2022Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answeringcited by 93
10 more
- 2021RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answeringcited by 86
- 2020KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Basecited by 78
- 2022Sequence-to-Sequence Knowledge Graph Completion and Question Answeringcited by 75
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Built on by
Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 2.
7 papers.
- 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaborationcited by 180
- 2023Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Modelscited by 11
- 2023Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learningcited by 6
- 2024RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questionscited by 2
- 2024Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerabilitycited by 1
- 2026When Correct Edges Cannot Be Verified: A Provenance Gap in Incomplete KGQA and a Provenance-Favoring Completion Policycited by 0
- 2026AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphscited by 0
- 2026AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs14 Aug 2026
- 2026When Correct Edges Cannot Be Verified: A Provenance Gap in Incomplete KGQA and a Provenance-Favoring Completion Policy14 Jun 2026
- 2024Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability20 Jun 2024
- 2024RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions16 Mar 2024
- 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration1 Feb 2024
- 2023Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning15 Nov 2023
- 2023Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models2 Oct 2023