Plugging Schema Graph into Multi-Table QA : A Human-Guided Framework for Reducing LLM Reliance (2025)

Abstract: Large language models (LLMs) have shown promise in table Question Answering (Table QA). However, extending these capabilities to multi-table QA remains challenging due to unreliable schema linking across complex tables. Existing methods based on semantic similarity work well only on simplified hand-crafted datasets and struggle to handle complex, real-world scenarios with numerous and diverse columns. To address this, we propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths. Given a natural language query, our method searches on graph to construct interpretable reasoning chains, aided by pruning and sub-path merging strategies to enhance efficiency and coherence. Experiments on both standard benchmarks and a realistic, large-scale dataset demonstrate the effectiveness of our approach. To our knowledge, this is the first multi-table QA system applied to truly complex industrial tabular data.

## introduction table question answering (table qa ) involves answering natural language questions using structured tabular data. this capability is increasingly critical in numerous practical domains, including healthcare @xcite , finance @xcite , and transportation @xcite , where data is commonly stored in relational databases @xcite or spreadsheet-like tables. effective table qa systems allow non-expert users to query structured data, thus enhancing accessibility, facilitating datadriven decision-making, and reducing the reliance on manual data manipulation or complex query languages such as sql. unlike traditional text-based qa @xcite , table qa requires models to understand the structural semantics of tabular data, including column types, row-level content, and inter-cell re- company … name id … … … table company game … … … … single-table qa multi-table qa ø graph assist method (ours) publish company game id … … … multi-table qa relevant information information filtering answer generation game … name id lol what is the name of company that published the game lol? game.@xmath0.game ✅ publish.@xmath1.name ✅ game.id game.name company.name company.id publish.game publish.company publish.id ø existing method user query • table embedding 𝑥 ! • query embedding 𝑦 • compute if 𝐶𝑜𝑠 𝑥 ! , 𝑦 > 𝜃 cos matching result 0.9 game.@xmath2.game 0.9 publish.@xmath3.id 0.8 game.@xmath4.name ✅ ❌ ✅ graph-searching result table qa paradigms. top: single-table qa, where all relevant information is contained within one table. middle: existing multi-table qa approaches that rely on semantic similarity for schema linking across tables. bottom: our proposed method, which leverages graph-based, human-curated relational knowledge to explicitly guide schema linking and inter-table reasoning. multi-table qa introduces greater complexity, as answering a question often requires reasoning over multiple related tables (middle of figure 1 ). this setting poses unique challenges such as identifying relevant tables, aligning schemas, and inferring join paths, which are not present in single-table qa. existing methods attempt to address these through flattening and concatenating multiple tables @xcite , leveraging key constraints for semantic alignment @xcite , or adopting multi-step retrieval pipelines @xcite . however, existing multi-table qa methods have been evaluated only on simplified benchmarks. for instance, the multi-table and multi-hop question answering (mmqa) benchmark @xcite ) contains tables with an average of just five columns. these are far less complex than realworld datasets like crash investigation sampling system (ciss) @xcite , where tables can have up to 207 columns. these methods typically rely on heavy model training to learn schema linking or semantic alignment, which may suffice for small benchmarks but becomes highly impractical for large-scale tables. even state-of-the-art llms struggle with accurate column-level matching at this scale @xcite . as schema linking across complex tables remains a major bottleneck, current approaches fail to generalize to realistic industrial scenarios. we argue that multi-table qa does not necessarily require complex model-based or semantic matching techniques to resolve schema linking across tables. in real-world table qa system, the set of tables and their structures are typically fixed. this allows us to leverage domain knowledge to manually construct an explicit graph that captures both schema linking and join path inference. by doing so, we bypass the major bottleneck of implicit schema alignment during inference. moreover, this approach enhances transparency and controllability of schema linking, making the overall multi-table qa pipeline more robust and easier to adapt to new domains without needing extensive retraining. importantly, the graph-based schema representation can be implemented as a plug-and-play module, enabling seamless integration with both closed-source and open-source llms. in this paper, we introduce sgam (schema graph-assisted multi-table qa), a graph-based framework that integrates human-curated schema knowledge to support multi-table qa, as shown in figure 1 (bottom). specifically, we construct a relational graph capturing the semantic and logical dependencies among table columns. each graph node encodes essential schema details, such as column name, associated table, and primary key status, while edges represent both intra-table semantic relationships and inter-table joinable or derivationbased links. given a natural language query, our method searches directed paths within this graph to derive clear and interpretable reasoning chains, sig-nificantly simplifying multi-table reasoning tasks. furthermore, we introduce efficient path pruning and sub-path merging strategies to minimize redundant joins and enhance inference coherence. our work offers three main contributions. first, we propose a novel graph-based framework for multi-table qa that leverages human-curated relational knowledge to explicitly represent schema linking and join paths, thereby greatly simplifying reasoning over complex real-world tables. second, we build and publicly release a new multi-table qa dataset based on the real-world ciss, which is substantially more realistic and challenging than existing benchmarks. finally, we achieve state-ofthe-art performance on both standard multi-table qa benchmarks and our new dataset. to the best of our knowledge, this is the first application of multitable qa to truly large-scale, real-world tabular data. ## problem description multi-table question answering involves answering a natural language question by retrieving and reasoning over multiple structured tables. let the table corpus be defined as @xmath5, . . . , t m }, where each table t i is represented as t @xmath6: a 1 , . . . , c d : a d }. here, c j denotes the name of the j-th column and a j its associated values, with d being the number of columns in t i . given a natural language question q, the system must identify a subset of relevant tables e(q) = {t q,1 , . . . , t q,k } ⊆ c and determine how to join them through appropriate conditions ▷◁ c j , such as foreign key relationships or semantically inferred links. these joins form a join path: the system must also retrieve a set of relevant attributes a(q) = {a 1 , a 2 , . . . , a n }, where each a j belongs to one of the selected tables, i.e., a @xmath7,j . the retrieved attributes and joined tables together must provide sufficient and relevant information to generate a natural language answer a to query q. table info. user query segments embedding model cosine similarity step 1: attribute-level retrieval step 2: graph-based information extraction avoid.caseid avoid.vehno occ.caseid occ.vehno gv.caseid gv.modelyr gv.vehno final answer: the driver's age is 88. necessary joints： • gv.@xmath8.caseid and gv.@xmath9.vehno • gv.@xmath10.caseid and gv.@xmath11.vehno relevant attributes relevant attributes： • gv.@xmath12.@xmath13.@xmath14.@xmath15.@xmath16.equip avoid.avail occ.rule occ.occno occ.age step 3 uses llm to generate a final answer based on extracted information. this graph-guided reasoning enables the system to accurately extract the target attribute (occ.age) by aligning semantically related fields and resolving multi-hop dependencies. in this case, the system correctly infers the driver's age as 88. to overcome these challenges, we propose a decomposition-based approach, explicitly segmenting each query into two distinct components: constraints and question focus. the constraints specify conditions to filter relevant records (e.g., particular vehicle models or case identifiers), while the question focus identifies the core information sought (e.g., activation of safety features, driver's age). handling these components separately allows more accurate retrieval and reduces interference from non-essential linguistic elements. retrieval tabular attributes: to support accurate attribute identification, our rag framework constructs structured keyword sets for each attribute. these keywords, derived from attribute names, descriptions, and representative values, fall into two categories: uniqueness-based keywords that uniquely identify an attribute, and frequency-based keywords that frequently occur within an attribute's values but rarely elsewhere. for instance, the term animal is typically associated with variables related to crashtype (crash type), while terms like truck and bus frequently occur in the bodycat (vehicle's body category) attribute. this design enables the retrieval process to harness both semantically distinctive signals and statistically salient patterns. given a query segment q, we embed it alongside each attribute's keyword set k i using a pre-trained embedding model, such as openai's text-embedding-3-small. the cosine similarity between the query and each keyword set is then computed as s @xmath17, emb(k i )). to better distinguish relevant from irrelevant attributes, similarity scores are squared and normalized: we select keywords exceeding a threshold (i.e., s ′ @xmath18, forming the retrieval context for subsequent rag processing. this method effectively balances recall and precision by exploiting both semantic relevance and frequency-based indicators, enhancing retrieval performance on noisy datasets. ## experiments we evaluate our graph-based method through two experiments: sql generation on the bird benchmark and end-to-end qa on the real-world ciss dataset. we now detail the setup and results foot_0 . ## conclusion in this work, we present a novel graph-based framework for multi-table question answering that integrates explicit, human-curated relational knowledge to assist in schema linking and reasoning. by constructing a structured relational graph that captures both intra-and inter-table dependencies, our method enables interpretable reasoning paths for complex queries, significantly improving robustness and scalability in real-world settings. we further introduce pruning and sub-path merging strategies to reduce redundancy and enhance inference coherence. to support real-world evaluation, we release a challenging multi-table qa dataset based on the ciss corpus. experiments on both standard benchmarks and our newly constructed dataset demonstrate the effectiveness of the proposed approach. by offloading structural and semantic understanding to a lightweight schema graph, our framework provides a scalable and interpretable solution that advances the practical deployment of multi-table qa in complex real-world industrial settings.