Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

2025-02-27

AI Summary

Existing retrieval methods for text-rich graph knowledge bases (TG-KBs) suffer from a disconnect between structural and textual knowledge access; hybrid approaches either neglect structural retrieval or underutilize neighborhood interactions. Method: We propose the Planning–Reasoning–Organizing (PRO) framework—the first to integrate *textualized planning graphs* into retrieval. PRO employs query-driven structural traversal planning, jointly performs graph path reasoning and fine-grained text matching, and leverages structural trajectories to enhance candidate re-ranking—enabling dynamic cross-modal alignment and complementary knowledge enhancement. The method unifies interpretable planning graph generation, multi-stage neural ranking, and semantic-structural joint modeling. Contribution/Results: PRO achieves significant improvements over state-of-the-art methods across multiple TG-KB benchmarks, empirically validating both the effectiveness of structural trajectory modeling for re-ranking and the robustness and generalizability of the hybrid retrieval paradigm.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• Enhances retrieval in Text-rich Graph Knowledge Bases
• Integrates structural and textual knowledge retrieval
• Proposes Planning-Reasoning-Organizing framework for queries

Innovation

• Mixture of Structural-and-Textual Retrieval
• Planning-Reasoning-Organizing framework
• Reranking based on structural trajectory