Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Semantic Text Processing

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Structured Data in NLP

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Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)

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Zhiqiang Zhang, Liqiang Wen, Wen Zhao

Semantic Text Processing

Challenge:	Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process.
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Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)

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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti

Semantic Text Processing

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