Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases (2025)

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.

## introduction text-rich graph knowledge bases (tg-kbs), due to their structured representation of textual documents, ubiquitously store textual and structural knowledge @xcite . for example, scholars retrieve relevant research from paper management systems to advance scientific discoveries where nodes represent papers and edges denote references @xcite . with large language models (llms)-powered generators approaching human intelligence in language comprehension and generation, retrieving supporting knowledge from tg-kbs to contextualize and ground generation has become increasingly crucial for correctly answering queries @xcite . since supporting knowledge in tg-kbs typically exhibits in both the textual and structural formats @xcite , retrieval methods should be tailored to both formats effectively as shown in figure 1 (a). textual retrieval methods retrieve textual knowledge such as indexed documents based on its similarity to the given query and can be broadly categorized into lexical methods (e.g., bm25) and semantic methods (e.g., contriever) @xcite . structural retrieval methods retrieve structural knowledge such as neighboring entities @xcite @xcite by applying graph traversal and graph machine learning @xcite . despite the advancements in both textual and structural retrieval, they are often applied independently and fail to mutually reinforce each other. as shown by figure 1 (b), neither structural retrieval by following the logical structure of the query nor textual retrieval by conducting top-k bm25 matching can achieve better performance on both amazon and mag datasets simultaneously. to effectively retrieve both textual and structural knowledge from tg-kbs, recent works @xcite aggregate neighboring documents to fuse structural knowledge into textual narratives, followed by textual retrieval, with @xcite filtering irrelevant neighbors by their relations and @xcite weighted aggregating neighbors based on their fields. however, three challenges remain. first, rewording aggregated neighbors requires frequently invoking llms, resulting in prohibitive resources for long documents with exponentially growing neighbors. second, structural signals humans use to form logical plans are completely discarded after neighbor aggregation. third, rigid neighbor aggregation overlooks varying desires for structural and textual knowledge for different queries and tg-kbs. even within the same tg-kb, such as mag in figure 1 (c), queries answered by textual retrieval (i.e., q text ) are different from those by structural retrieval (i.e., q struct ). to address the above three challenges, we infuse the mixture-of-expert philosophy into retrieval design and propose a mixture of @xcite in figure 2 . mor begins with a planning module that generates planning graphs to outline query logics and preserve structural signals without rewording aggregated neighbors, overcoming the first and second challenges. next, mor interleaves structural traversal and textual matching in the reasoning module, enabling these two retrievals to reinforce each other. finally, mor devises a structure-aware reranker in the organization module that adaptively adjusts the importance of retrieved textual/structural knowledge, addressing the third challenge. via planning-reasoning-organizing, mor intelligently retrieves structural and textual knowledge based on query logical structure. our key contributions are: ## preliminary notations: a text-rich graph knowledge base (tg-kb) b generally consists of a set of connected nodes v in the graph with each node @xmath0. when retrieving nodes with supporting documents from b for answering a given query @xmath1, we typically follow certain rationale encapsulating the underlying logic of that query @xcite , which can be characterized by a text-attributed planning graph g. in many existing works @xcite , this planning graph can be usually decomposed into multiple reasoning problem setup: with the above notations, the investigated problem is to retrieve entities c ⊆ v answering a given query q. textual retrieval retrieves candidates based on the textual signals of both the query and documents. one common strategy is to retrieve candidates c from the whole documents d that have top-k textual similarity to query q measured by lexical or semantic similarity @xcite . the textual retrieval used in mor retrieves documents for a given query by matching them with textual descriptions in the query, e.g., matching stellar populations in tidal tails shown in figure 1 . structural retrieval retrieves candidates by applying prescribed rules to structured databases such as knowledge graphs and sql @xcite . common strategies include graph-based traversal (e.g., bfs, dfs) and rule fetching @xcite . specifically, mor conducts structural retrieval by traversing neighbors of certain categories from the generated planning graph. for example, in figure 1 (a), only "paper" typed neighbors of the author can be traversed by our structural retrieval. ## framework in a nutshell, we formulate our mor as the conditional distribution p θ (c|q, b) of retrieved candidates c given the user input query q over tg-kb b, which is further factorized into three distributions corresponding to our proposed three modules: planning via generating the text-attributed planning graph g, reasoning via conducting mixture of structural-and-textual traversal to obtain intermediate candidates c following the generated planning graph g, and organizing via applying structureaware reranking to the obtained candidates c, obtaining final candidates c: planning where p θ 1 (g|q, b) is the probability distribution of generating the text-attributed planning graph g given the input query q and tg-kb b; p θ 2 ( c|g, q, b) is the probability distribution of retrieving intermediate candidates c given the planning graph g and the query q via our mixed traversal; p θ 3 (c| c, g, q, b) is the probability distribution of reranking the intermediate candidates so that top-k positions form the ground-truth entities c. g/c denotes the space of all possible planning graphs and all possible configurations of size-k candidate node sets from all nodes v in tg-kb b. the overall objective is to maximize the likelihood of retrieving ground-truth candidates c for each input query @xmath2: following the above paradigm, we next introduce the three components: planning via textual graph generation in section 3.1, reasoning via mixed traversal in section 3.2, and organizing via structure-aware reranking in section 3.3. ## conclusion in this work, we propose a mixture of structural and textual retrieval (mor) to adaptively retrieve structural and textual knowledge based on query desire, which first utilizes a textual graph generator to generate the planning graph, then performs a mixed traversal and conducts organizing via a structureaware reranker to obtain final candidates. experiments demonstrate the advantages of our mor in harmonizing the retrieval of both textual and structural knowledge with insightful discoveries, including balancing retrieval performance across queries with different patterns and query-adaptive knowledge desire for structural/textual knowledge.