title: Mixture of Structural-and-Textual Retrieval over Text-rich Graph
    Knowledge Bases
summary: 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 some hybrid
  methods even bypass structural retrieval entirely after neighboring
  aggregation. To fill in 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 insights, including uneven
  retrieving performance across different query logics and the benefits of
  integrating structural trajectories for candidate reranking. Our code is
  available at https://github.com/Yoega/MoR.
paper_authors._id: 67c95b2f1fcfdc62ba3a620b
paper_authors.name: Yongjia Lei
paper_authors._id: 67c95b2f1fcfdc62ba3a620c
paper_authors.name: Haoyu Han
paper_authors._id: 67c95b2f1fcfdc62ba3a620d
paper_authors.name: Ryan A. Rossi
paper_authors._id: 67c95b2f1fcfdc62ba3a620e
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T09:24:22.970Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 67c95b2f1fcfdc62ba3a620f
paper_authors.name: Nedim Lipka
paper_authors._id: 67c95b2f1fcfdc62ba3a6210
paper_authors.name: Mahantesh M Halappanavar
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-06T08:22:09.090Z
paper_authors._id: 637c6d95a8716d642050b50f
paper_authors.avatarUrl: /avatars/0955a10113807348f24db968c7bd7c7a.svg
paper_authors.fullname: Mahantesh Halappanavar
paper_authors.type: user
paper_authors.user: mhalappa
paper_authors._id: 67c95b2f1fcfdc62ba3a6211
paper_authors.name: Jiliang Tang
paper_authors._id: 67c95b2f1fcfdc62ba3a6212
paper_authors.name: Yu Wang
paper_title: Mixture of Structural-and-Textual Retrieval over Text-rich Graph
    Knowledge Bases
paper_summary: 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 some hybrid
  methods even bypass structural retrieval entirely after neighboring
  aggregation. To fill in 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 insights, including uneven
  retrieving performance across different query logics and the benefits of
  integrating structural trajectories for candidate reranking. Our code is
  available at https://github.com/Yoega/MoR.
paper_ai_keywords: Text-rich Graph Knowledge Bases (TG-KBs) retrieval methods Mixture of Structural-and-Textual Retrieval (MoR) Planning-Reasoning-Organizing framework textual planning graphs structural traversal textual matching structural trajectory