Mixture of Structural-and-Textual Retrieval over Text-rich Graph
  Knowledge Bases
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.
67c95b2f1fcfdc62ba3a620b
Yongjia Lei
67c95b2f1fcfdc62ba3a620c
Haoyu Han
67c95b2f1fcfdc62ba3a620d
Ryan A. Rossi
67c95b2f1fcfdc62ba3a620e
Franck Dernoncourt
claimed_verified
2025-03-06T09:24:22.970Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
67c95b2f1fcfdc62ba3a620f
Nedim Lipka
67c95b2f1fcfdc62ba3a6210
Mahantesh M Halappanavar
extracted_confirmed
2025-05-30T05:25:25.965Z
637c6d95a8716d642050b50f
/avatars/0955a10113807348f24db968c7bd7c7a.svg
Mahantesh Halappanavar
user
mhalappa
67c95b2f1fcfdc62ba3a6211
Jiliang Tang
67c95b2f1fcfdc62ba3a6212
Yu Wang
Mixture of Structural-and-Textual Retrieval over Text-rich Graph
  Knowledge Bases
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.
A Mixture of Structural-and-Textual Retrieval (MoR) framework integrates textual and structural knowledge retrieval using a Planning-Reasoning-Organizing framework, enhancing query answering performance in Text-rich Graph Knowledge Bases (TG-KBs).
Mixture of Structural-and-Textual Retrieval MoR Planning-Reasoning-Organizing framework textual planning graphs structural traversal textual matching TG-KBs candidate reranking structural trajectory