@inproceedings{lei-etal-2025-mixture,
title = "Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases",
author = "Lei, Yongjia and
Han, Haoyu and
Rossi, Ryan A. and
Dernoncourt, Franck and
Lipka, Nedim and
Halappanavar, Mahantesh M and
Tang, Jiliang and
Wang, Yu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.941/",
doi = "10.18653/v1/2025.findings-acl.941",
pages = "18306--18321",
ISBN = "979-8-89176-256-5",
abstract = "Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and existing hybrid methods even bypass structural retrieval entirely. To fill this gap, we propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework. In the Planning stage, MoR generates textual planning graphs delineating the logic for answering queries. Following planning graphs, in the Reasoning stage, MoR interweaves structural traversal and textual matching to obtain candidates from TG-KBs. In the Organizing stage, MoR further reranks fetched candidates based on their structural trajectory. Extensive experiments demonstrate the superiority of MoR in harmonizing structural and textual retrieval with inspiring insights, including imbalanced retrieving performance across different query logics and the benefits of integrating structural trajectories for candidate reranking."
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<abstract>Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and existing hybrid methods even bypass structural retrieval entirely. To fill this gap, we propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework. In the Planning stage, MoR generates textual planning graphs delineating the logic for answering queries. Following planning graphs, in the Reasoning stage, MoR interweaves structural traversal and textual matching to obtain candidates from TG-KBs. In the Organizing stage, MoR further reranks fetched candidates based on their structural trajectory. Extensive experiments demonstrate the superiority of MoR in harmonizing structural and textual retrieval with inspiring insights, including imbalanced retrieving performance across different query logics and the benefits of integrating structural trajectories for candidate reranking.</abstract>
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%0 Conference Proceedings
%T Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases
%A Lei, Yongjia
%A Han, Haoyu
%A Rossi, Ryan A.
%A Dernoncourt, Franck
%A Lipka, Nedim
%A Halappanavar, Mahantesh M.
%A Tang, Jiliang
%A Wang, Yu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F lei-etal-2025-mixture
%X Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and existing hybrid methods even bypass structural retrieval entirely. To fill this gap, we propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework. In the Planning stage, MoR generates textual planning graphs delineating the logic for answering queries. Following planning graphs, in the Reasoning stage, MoR interweaves structural traversal and textual matching to obtain candidates from TG-KBs. In the Organizing stage, MoR further reranks fetched candidates based on their structural trajectory. Extensive experiments demonstrate the superiority of MoR in harmonizing structural and textual retrieval with inspiring insights, including imbalanced retrieving performance across different query logics and the benefits of integrating structural trajectories for candidate reranking.
%R 10.18653/v1/2025.findings-acl.941
%U https://aclanthology.org/2025.findings-acl.941/
%U https://doi.org/10.18653/v1/2025.findings-acl.941
%P 18306-18321
Markdown (Informal)
[Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases](https://aclanthology.org/2025.findings-acl.941/) (Lei et al., Findings 2025)
ACL
- Yongjia Lei, Haoyu Han, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Mahantesh M Halappanavar, Jiliang Tang, and Yu Wang. 2025. Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases. In Findings of the Association for Computational Linguistics: ACL 2025, pages 18306–18321, Vienna, Austria. Association for Computational Linguistics.