@inproceedings{hosseini-etal-2026-pema,
title = "{P}e{MA}-{KGQA}: A Multi-Agent {LLM} Framework for {P}ersian Knowledge Graph Question Answering",
author = "Hosseini, Kimia and
Hosseini, Mohammad and
Momtazi, Saeedeh",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.30/",
pages = "560--579",
ISBN = "979-8-89176-444-6",
abstract = "Knowledge Graph Question Answering (KGQA) is particularly challenging for Persian, a complex and low-resource language with significant ambiguity. To address this, we introduce PeMA-KGQA (Persian Multi-Agent Knowledge Graph Question Answering), the first multi-agent LLM framework for Persian KGQA, arguing that an agent-based approach is essential for robust reasoning. Our system operates on FarsBase and is evaluated on both the ParsSimpleQA (one-hop) and PeCoQ (compositional) benchmarks. The framework employs a plan-and-execute paradigm where a dedicated Planner agent first decomposes a question into a structured executable plan using a toolbox of operators. Subsequent specialized agents{---}an Entity Grounder, a Relation Selector, and a Path Scorer{---}then execute this plan, effectively grounding mentions, selecting relations, and scoring reasoning paths to produce the final answer. This multi-agent decomposition provides a transparent and interpretable reasoning trace. PeMA-KGQA sets a new state-of-the-art on ParsSimpleQA with 21.14{\%} improvement in Hits@5 and achieves 80.67{\%} in Hits@5 on the complex multi-hop questions of PeCoQ."
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<abstract>Knowledge Graph Question Answering (KGQA) is particularly challenging for Persian, a complex and low-resource language with significant ambiguity. To address this, we introduce PeMA-KGQA (Persian Multi-Agent Knowledge Graph Question Answering), the first multi-agent LLM framework for Persian KGQA, arguing that an agent-based approach is essential for robust reasoning. Our system operates on FarsBase and is evaluated on both the ParsSimpleQA (one-hop) and PeCoQ (compositional) benchmarks. The framework employs a plan-and-execute paradigm where a dedicated Planner agent first decomposes a question into a structured executable plan using a toolbox of operators. Subsequent specialized agents—an Entity Grounder, a Relation Selector, and a Path Scorer—then execute this plan, effectively grounding mentions, selecting relations, and scoring reasoning paths to produce the final answer. This multi-agent decomposition provides a transparent and interpretable reasoning trace. PeMA-KGQA sets a new state-of-the-art on ParsSimpleQA with 21.14% improvement in Hits@5 and achieves 80.67% in Hits@5 on the complex multi-hop questions of PeCoQ.</abstract>
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%0 Conference Proceedings
%T PeMA-KGQA: A Multi-Agent LLM Framework for Persian Knowledge Graph Question Answering
%A Hosseini, Kimia
%A Hosseini, Mohammad
%A Momtazi, Saeedeh
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F hosseini-etal-2026-pema
%X Knowledge Graph Question Answering (KGQA) is particularly challenging for Persian, a complex and low-resource language with significant ambiguity. To address this, we introduce PeMA-KGQA (Persian Multi-Agent Knowledge Graph Question Answering), the first multi-agent LLM framework for Persian KGQA, arguing that an agent-based approach is essential for robust reasoning. Our system operates on FarsBase and is evaluated on both the ParsSimpleQA (one-hop) and PeCoQ (compositional) benchmarks. The framework employs a plan-and-execute paradigm where a dedicated Planner agent first decomposes a question into a structured executable plan using a toolbox of operators. Subsequent specialized agents—an Entity Grounder, a Relation Selector, and a Path Scorer—then execute this plan, effectively grounding mentions, selecting relations, and scoring reasoning paths to produce the final answer. This multi-agent decomposition provides a transparent and interpretable reasoning trace. PeMA-KGQA sets a new state-of-the-art on ParsSimpleQA with 21.14% improvement in Hits@5 and achieves 80.67% in Hits@5 on the complex multi-hop questions of PeCoQ.
%U https://aclanthology.org/2026.findings-aacl.30/
%P 560-579
Markdown (Informal)
[PeMA-KGQA: A Multi-Agent LLM Framework for Persian Knowledge Graph Question Answering](https://aclanthology.org/2026.findings-aacl.30/) (Hosseini et al., Findings 2026)
ACL