Papers with CWQ
CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)
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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang
| Challenge: | Existing knowledge base question answering methods struggle with complex queries. |
| Approach: | They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ. |
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)
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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu
| Challenge: | Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors. |
| Approach: | They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly. |
| Outcome: | Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ. |
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)
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| Challenge: | Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task. |
| Approach: | They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision. |
| Outcome: | The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods . |
GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025.findings-emnlp)
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| Challenge: | Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution. |
| Approach: | a framework that decouples logical structure generation from semantic grounding is proposed . the framework explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures. |
| Outcome: | GRV-KBQA decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy. |
RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy. |
| Approach: | They propose a retrieval-generate-retrieve framework that uses a Retrieve-Generate framework to retrieve factual knowledge from a knowledge graph. |
| Outcome: | Experimental results show that RGR-KBQA improves on CWQ and WebQSP datasets. |
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. |
| Approach: | They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments. |
| Outcome: | The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods. |
A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop Reasoning (2022.naacl-main)
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| Challenge: | Existing knowledge based question answering systems are trained based on labeled reasoning paths, which hinder their performance. |
| Approach: | They propose a KBQA system which leverages multiple reasoning paths’ information and only requires labeled answer as supervision. |
| Outcome: | The proposed system can leverage multiple reasoning paths’ information and only requires labeled answer as supervision. |
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question. |
| Approach: | They propose a method that starts with the entire knowledge base and gradually shrinks it to the desired query graph. |
| Outcome: | Experimental results show that the proposed method achieves state-of-the-art performance on ComplexWebQuestion dataset. |
Graph Explorer: Training Faithful KG Agents with Visibility-Grounded Supervision (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are strong reasoners but still hallucinate and make unreliable decisions on knowledge-intensive questions. |
| Approach: | They propose a pipeline that turns LLM into executable tool supervision without manual trace labeling. |
| Outcome: | The proposed model improves over a reproduced prompting baseline by +22.5/+16.2 points . it is based on a Graph Explorer pipeline that turns SPARQL into executable tool supervision without manual trace labeling. |
Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning (2026.findings-acl)
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| Challenge: | Existing Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) systems insufficiently model the interaction between query semantics and relation types, resulting in imprecise subgraph retrieval and unstable reasoning. |
| Approach: | They propose a retrieval framework that integrates query semantics and relation embeddings directly into the attention mechanism. |
| Outcome: | Experiments on WebQSP and CWQ establish new state-of-the-art results in both Triple Recall and Answer Recall. |
ReKG-MCTS: Reinforcing LLM Reasoning on Knowledge Graphs via Training-Free Monte Carlo Tree Search (2025.findings-acl)
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| Challenge: | Existing approaches to combining knowledge graphs with large language models face limitations in path exploration strategies or excessive computational overhead. |
| Approach: | They propose a training-free framework that synergizes Monte Carlo Tree Search with LLM capabilities to enable dynamic reasoning over KGs. |
| Outcome: | The proposed framework outperforms existing training-free methods and achieves competitive performance compared to fine-tuned baselines. |
Reasoning with Ontology Graph: Toward Type-Constrained Knowledge Graph Question Answering (2026.acl-long)
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| Challenge: | Existing knowledge graph question answering methods rely on LLM-induced type systems with inconsistent granularity or perform multi-hop reasoning without explicit target-type constraints. |
| Approach: | They propose a type-constrained knowledge graph question answering framework that reasons over a relation-centric ontology graph. |
| Outcome: | The proposed framework achieves state-of-the-art and produces ontology-grounded reasoning chains with substantial Hit@1 gains. |
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)
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| Challenge: | Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance . |
| Approach: | They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation . |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks. |
GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs (2025.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation (RAG) rely on costly LLM calls to generate relation paths or traverse the KG. |
| Approach: | They propose a framework that uses lightweight Graph Neural Networks to enhance retrieval. |
| Outcome: | The proposed framework outperforms existing methods on multi-hop and multi-entity questions. |
SAFE: Schema-Driven Approximate Distance Join for Efficient Knowledge Graph Querying (2025.emnlp-main)
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| Challenge: | Existing methods map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph. |
| Approach: | They propose a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval. |
| Outcome: | Extensive experiments on WebQSP, CWQ and GrailQA show that the proposed framework outperforms state-of-the-art methods in accuracy and efficiency. |
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing retrieval-based approaches to solve multihop Knowledge Base Question Answering (KBQA) fail to utilize information from head-tail entities and the semantic connection between relations to enhance the information capturing of relations in KGs. |
| Approach: | They propose to use a dual relation graph to find the answer entity in a knowledge graph . they use primal entity graph reasoning, dual relation grafitment and interaction . |
| Outcome: | The proposed approach achieves significant performance gain over the prior state-of-the-art on two public datasets, WebQSP and CWQ. |
Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens. |
| Approach: | They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability. |
| Outcome: | The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations. |
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing KG-RAG systems collapse all reasoning hops into a single representation, flat embedding space, suppressing this implicit structure and causing noisy or drifted path exploration. |
| Approach: | They propose a symmetric multi-view framework that decouples queries and KGs into aligned, head-specific retrieval spaces. |
| Outcome: | The proposed framework achieves state-of-the-art retrieval and QA performance on WebQSP and CWQ, and significantly reduces hallucination. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)
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| Challenge: | Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility. |
| Approach: | They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem. |
| Outcome: | The proposed framework captures the trade-off between information relevance and structural complexity. |
D-RAG: Differentiable Retrieval-Augmented Generation for Knowledge Graph Question Answering (2025.emnlp-main)
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Guangze Gao, Zixuan Li, Chunfeng Yuan, Jiawei Li, Wu Jianzhuo, Yuehao Zhang, Xiaolong Jin, Bing Li, Weiming Hu
| Challenge: | Existing approaches to Knowledge Graph Question Answering (KGQA) use Retrieval-Augmented Generation (RAG) but subgraph selection process is non-differentiable, preventing end-to-end training of the retriever and the generator. |
| Approach: | They propose a Differentiable RAG approach that optimizes the retriever and the generator for KGQA. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on WebQSP and CWQ. |