Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline . |
| Approach: | They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs. |
| Outcome: | Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient. |
Similar Papers
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)
Copied to clipboard
| 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. |
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
| Outcome: | Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete. |
KET-QA: A Dataset for Knowledge Enhanced Table Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing datasets that ignore the challenge of missing knowledge in TableQA are limited in their use. |
| Approach: | They propose to use a knowledge base as the external knowledge source for TableQA and construct a dataset with fine-grained gold evidence annotation. |
| Outcome: | The proposed model achieves remarkable performance improvements on three different settings, but still lags behind the human-level performance. |
Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)
Copied to clipboard
Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang
| Challenge: | Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks. |
| Approach: | They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting. |
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)
Copied to clipboard
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. |
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)
Copied to clipboard
| 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. |
RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)
Copied to clipboard
| 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. |
FeTaQA: Free-form Table Question Answering (2022.tacl-1)
Copied to clipboard
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev
| Challenge: | Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources. |
| Approach: | They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer. |
| Outcome: | The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. |
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)
Copied to clipboard
| Challenge: | Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time. |
| Approach: | They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking. |
| Outcome: | The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient. |
Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap. |
| Approach: | They propose a flexible framework that leverages LLMs’ prior knowledge to enrich KGs and bridge the semantic gap between queries and graphs. |
| Outcome: | The proposed framework bridges the semantic gap between structured knowledge graphs and unstructured queries while ensuring low computational costs, scalability, and adaptability across different methods. |