@inproceedings{sun-kazakov-2025-kgeir,
title = "{KGEIR}: Knowledge Graph-Enhanced Iterative Reasoning for Multi-Hop Question Answering",
author = "Sun, Tianda and
Kazakov, Dimitar",
editor = "Picazo-Izquierdo, Alicia and
Estevanell-Valladares, Ernesto Luis and
Mitkov, Ruslan and
Guillena, Rafael Mu{\~n}oz and
Cerd{\'a}, Ra{\'u}l Garc{\'i}a",
booktitle = "Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.r2lm-1.14/",
pages = "134--143",
abstract = "Multi-hop question answering (MHQA) requires systems to retrieve and connect information across multiple documents, a task where large language models often struggle. We introduce Knowledge Graph-Enhanced Iterative Reasoning (KGEIR), a framework that dynamically constructs and refines knowledge graphs during question answering to enhance multi-hop reasoning. KGEIR identifies key entities from questions, builds an initial graph from retrieved paragraphs, reasons over this structure, identifies information gaps, and iteratively retrieves additional context to refine the graph until sufficient information is gathered. Evaluations on HotpotQA, 2WikiMultiHopQA, and MuSiQue benchmarks show competitive or superior performance to state-of-the-art methods. Ablation studies confirm that structured knowledge representations significantly outperform traditional prompting approaches like Chain-of-Thought and Tree-of-Thought. KGEIR{'}s ability to explicitly model entity relationships while addressing information gaps through targeted retrieval offers a promising direction for integrating symbolic and neural approaches to complex reasoning tasks."
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<abstract>Multi-hop question answering (MHQA) requires systems to retrieve and connect information across multiple documents, a task where large language models often struggle. We introduce Knowledge Graph-Enhanced Iterative Reasoning (KGEIR), a framework that dynamically constructs and refines knowledge graphs during question answering to enhance multi-hop reasoning. KGEIR identifies key entities from questions, builds an initial graph from retrieved paragraphs, reasons over this structure, identifies information gaps, and iteratively retrieves additional context to refine the graph until sufficient information is gathered. Evaluations on HotpotQA, 2WikiMultiHopQA, and MuSiQue benchmarks show competitive or superior performance to state-of-the-art methods. Ablation studies confirm that structured knowledge representations significantly outperform traditional prompting approaches like Chain-of-Thought and Tree-of-Thought. KGEIR’s ability to explicitly model entity relationships while addressing information gaps through targeted retrieval offers a promising direction for integrating symbolic and neural approaches to complex reasoning tasks.</abstract>
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%0 Conference Proceedings
%T KGEIR: Knowledge Graph-Enhanced Iterative Reasoning for Multi-Hop Question Answering
%A Sun, Tianda
%A Kazakov, Dimitar
%Y Picazo-Izquierdo, Alicia
%Y Estevanell-Valladares, Ernesto Luis
%Y Mitkov, Ruslan
%Y Guillena, Rafael Muñoz
%Y Cerdá, Raúl García
%S Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F sun-kazakov-2025-kgeir
%X Multi-hop question answering (MHQA) requires systems to retrieve and connect information across multiple documents, a task where large language models often struggle. We introduce Knowledge Graph-Enhanced Iterative Reasoning (KGEIR), a framework that dynamically constructs and refines knowledge graphs during question answering to enhance multi-hop reasoning. KGEIR identifies key entities from questions, builds an initial graph from retrieved paragraphs, reasons over this structure, identifies information gaps, and iteratively retrieves additional context to refine the graph until sufficient information is gathered. Evaluations on HotpotQA, 2WikiMultiHopQA, and MuSiQue benchmarks show competitive or superior performance to state-of-the-art methods. Ablation studies confirm that structured knowledge representations significantly outperform traditional prompting approaches like Chain-of-Thought and Tree-of-Thought. KGEIR’s ability to explicitly model entity relationships while addressing information gaps through targeted retrieval offers a promising direction for integrating symbolic and neural approaches to complex reasoning tasks.
%U https://aclanthology.org/2025.r2lm-1.14/
%P 134-143
Markdown (Informal)
[KGEIR: Knowledge Graph-Enhanced Iterative Reasoning for Multi-Hop Question Answering](https://aclanthology.org/2025.r2lm-1.14/) (Sun & Kazakov, R2LM 2025)
ACL