@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://preview.aclanthology.org/acl25-workshop-ingestion/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."
}