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Knowledge graphs serve as structured data sources for question answering systems, as evidenced by research on KG-based QA methods [1], [2], and [3]. Furthermore, frameworks like KA-RAG [4] and PoG [5] integrate these graphs with LLMs to enhance reasoning capabilities for complex question answering tasks.
Facts (11)
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A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 4 facts
referenceZhang Y, Dai H, Kozareva Z, Smola A, and Song L published 'Variational reasoning for question answering with knowledge graph' in the Proceedings of the AAAI Conference on Artificial Intelligence in 2018.
referenceZhang, Dai, Kozareva, Smola, and Song authored 'Variational reasoning for question answering with knowledge graph', published in the Proceedings of the AAAI Conference on Artificial Intelligence in 2018 (Volume 32, Issue 1).
referenceThe FreebaseQA benchmark evaluates question answering using the Freebase knowledge graph by testing the ability of models to answer questions through querying, providing a measure of their ability to handle large-scale structured data.
claimLLMs facilitate KG-to-text generation and question-answering by generating human-like descriptions of facts stored within a knowledge graph.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 3 facts
referenceThe KG-CoT method (Zhao et al., 2024b) leverages external knowledge graphs to generate reasoning paths for joint reasoning of Large Language Models and knowledge graphs to enhance reasoning capabilities for question answering.
claimPoG (Chen et al., 2024a) integrates reflection and self-correction mechanisms to adaptively explore reasoning paths over a knowledge graph via an LLM agent, augmenting the LLM in complex reasoning and question answering.
procedureXiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, and Wei Hu (2025) conducted a literature review by retrieving research papers published since 2021 using Google Scholar and PaSa, utilizing search phrases such as 'knowledge graph and language model for question answering' and 'KG and LLM for QA', while extending the search scope for benchmark dataset papers to 2016.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
referenceXu et al. (2024) introduced 'Generate-on-Graph', a method that treats large language models as both an agent and a knowledge graph for incomplete knowledge graph question answering.
The construction and refined extraction techniques of knowledge ... nature.com 1 fact
referenceSingh, K. et al. published 'No one is perfect: analysing the performance of question answering components over the dbpedia knowledge graph' in J. Web Semant. 65, 100594 (2020).
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org 1 fact
referenceXiaofeng Huang, Jixin Zhang, Zisang Xu, Lu Ou, and Jianbin Tong published 'A knowledge graph based question answering method for medical domain' in 2021.
KA-RAG: Integrating Knowledge Graphs and Agentic Retrieval ... semanticscholar.org 1 fact
claimKA-RAG is a course-oriented question answering (QA) framework that integrates a structured knowledge graph with agentic retrieval-augmented generation.