Mixture of Structural-and-Textual Retrieval over Text-rich Graph
  Knowledge Bases
Authors: name: Yongjia Lei, name: Haoyu Han, name: Ryan A. Rossi, name: Franck Dernoncourt, name: Nedim Lipka, name: Mahantesh M Halappanavar, name: Jiliang Tang, name: Yu Wang

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.