GRV - KBQA : A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025)

Abstract: Knowledge Base Question Answering (KBQA) is a fundamental task that enables natural language interaction with structured knowledge bases (KBs).Given a natural language question, KBQA aims to retrieve the answers from the KB. However, existing approaches, including retrieval-based, semantic parsing-based methods and large-language model-based methods often suffer from generating non-executable queries and inefficiencies in query execution. To address these challenges, we propose GRV-KBQA, a three-stage framework that decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy. Unlike previous methods, GRV-KBQA explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures. Experimental results on WebQSP and CWQ show that GRV-KBQA significantly improves performance over existing approaches. The ablation study conducted confirms the effectiveness of the decoupled logical form generation and validation mechanism of our framework.

## introduction knowledge bases (kbs) such as freebase @xcite , wikidata @xcite , and dbpedia @xcite , which contain vast amounts of structured data in the form of triples, are widely used due to their structured nature and the accuracy of the information they represent. knowledge base questioning answering (kbqa) is a popular application of kb, aiming to retrieve accurate answers from the kb for a given question. previous methods for knowledge base question answering (kbqa) can be broadly categorized into two main classes: information retrievalbased (ir-based) @xcite several research efforts have proposed sequence-tosequence (seq-to-seq) approaches based on llms, which directly generate logical forms, align them to the kb, and then execute them on the kb to retrieve answers. kb-coder @xcite and arg-kbqa @xcite have leveraged llms for few-shot in-context learning, where a small number of examples guides the model to generate logical forms. to fully leverage the capabilities of llms while avoiding the use of black-box models, chatkbqa @xcite fine-tunes open-source models to enable them to generate logical forms based on the given questions. however, existing methods still face two key issues: 1. challenges in generating correct logical forms: they suffer from generating nonexecutable logical forms in many cases. for example, the basic logical structure of generated logical form is wrong, as shown in figure 1 . this limitation primarily arises from the one-step generation paradigm, which requires the model to simultaneously learn both the structural composition of the query and the semantic grounding of its components. this joint learning process increases the model's cognitive load, making it more prone to errors in both aspects, which can lead to errors in logical forms, including incorrect logical form structures (e.g., incorrect nesting of operations or missing/extra filtering conditions) and semantic mismatches (e.g., generating non-existent entities or relations). these errors often make the generated logical forms non-executable, preventing the framework from retrieving the correct answers. 2. inefficiency in aligning with the knowledge base: existing methods, when executing the generated logical form, require aligning the generated entities and relations to kb. however, previous methods generate all possible combinations of candidates without considering available pruning optimizations, as shown in figure 1 , which would result in an enormous search space, causing significant time to be wasted on invalid queries and greatly slowing down the query process. to address these issues, we propose grv-kbqa: a three-stage framework for knowledge base question answering with decoupled logical structure generation, semantic grounding, and structure-aware validation. for challenges in generating correct logical forms, we introduce decoupled logical structure generation, which simplifies the generation of nested logical structures by employing a two-phase compositional approach. this method leverages a subquery chain template with placeholders for entities, relations, and intermediate results, which are then specified based on the question semantics and instantiated with corresponding kb elements to produce the complete logical form. for inefficiency in aligning with the knowledge base, we propose structure-aware validation, which employ structure-aware validation techniques to verify the consistency and correctness of the relations and entities in the logical form, ensuring that the structure of the logic form complies with the constraints of the kb. we conduct experiments on two widely used datasets, webqsp @xcite and cwq @xcite , and the results demonstrate that grv-kbqa outperforms other baselines on these datasets, highlighting the effectiveness of decoupled logical structure generation and structure-aware validation. in summary, our contributions are as follows: ## related work traditional knowledge base question answering (kbqa) methods can generally be divided into two main categories: information retrieval (ir)-based methods and semantic parsing (sp)-based methods. in the era of llms, several methods have emerged that leverage llms to tackle the kbqa task. ir-based methods @xcite @xcite @xcite focus on extracting a subgraph from the kb that is specific to the given question, and then apply ranking algorithms to identify the top entities or employ text decoders to directly generate the answers. ## experiments in this section, we introduce the basic setup of the experiments, including the datasets, baselines, and implementation details. we also provide an analysis of the experimental results. ## ablation study in order to assess the contribution of each component in grv-kbqa, we conduct an ablation study by systematically removing or modifying key steps and observing the impact on performance. specifically, we design a series of experiments to evaluate the effectiveness of each component. specifically, we conduct our ablation experiments on the we-bqsp dataset. ## conclusion in this paper, we propose grv-kbqa, a threestage framework for knowledge base question answering (kbqa) that decouples logical structure generation from semantic grounding while incorporating structure-aware validation. existing kbqa approaches, struggle with errors in logical forms, incorrect entity and relation alignment, or inefficiencies in candidate selection, leading to execution failures. our framework addresses these challenges by explicitly modeling the logical form through subquery chain generation and placeholder specification, and ensuring logical form validity through structural constraints. experimental results on webqsp and cwq show that grv-kbqa significantly improves performance over existing approaches, which demonstrates its effectiveness in generating more accurate and executable logical forms. the ablation study conducted further validates that decoupling logical structure generation from semantic grounding improves entity alignment, while structure-aware validation enhances execution reliability.