Knowledge Graph Question Answering

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 13 facts

claimIntegrating Large Language Models with Knowledge Graphs, as demonstrated in Chain-of-Knowledge and G-Retriever, enhances precision and efficiency in Knowledge Graph Question Answering.

formulaIn Knowledge Graph Question Answering, a multi-hop traversal path is represented as p = (e_0, r_1, e_1, r_2, ..., r_n, e_n), where e_i denotes the i-th entity, r_i denotes the i-th relation, and n denotes the length of the path.

procedureThe Information Retrieval (IR) process in Knowledge Graph Question Answering entails locating and extracting relevant paths through nodes and relationships within the Knowledge Graph that lead to the answer sought by the query.

claimThe Chain of Explorations (CoE) is a method for knowledge graph question answering (KGQA) that systematically extracts pertinent information from a knowledge graph by performing a sequential traversal of nodes and relationships.

claimThe ComplexWebQuestions (CWQ) dataset is designed to test knowledge graph question answering (KGQA) frameworks by including complex queries that require multi-hop reasoning, temporal constraints, and aggregations.

claimKnowledge Graph Question Answering systems have evolved from rule-based systems to sophisticated architectures capable of handling diverse question types.

claimThe ComplexWebQuestions (CWQ) dataset is designed to test knowledge graph question answering (KGQA) frameworks by including complex queries that require multi-hop reasoning, temporal constraints, and aggregations.

claimKnowledge Graph Question-Answering (KGQA) is a reasoning task that leverages knowledge graphs to retrieve correct answers for natural language questions by extracting knowledge from the graph.

formulaIn Knowledge Graph Question Answering, a multi-hop traversal path is represented as p = (e_0, r_1, e_1, r_2, ..., r_n, e_n), where e_i denotes the i-th entity, r_i denotes the i-th relation, and n denotes the length of the path.

claimThe Chain of Explorations (CoE) is a method for knowledge graph question answering (KGQA) that systematically extracts pertinent information from a knowledge graph by performing a sequential traversal of nodes and relationships.

claimIntegrating Large Language Models with Knowledge Graphs, as demonstrated in Chain-of-Knowledge and G-Retriever, enhances precision and efficiency in Knowledge Graph Question Answering.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 6 facts

claimKnowledge graph-based question-answering systems increase efficiency by focusing on entities with relevant properties and semantics rather than searching massive textual data, thereby reducing the search space.

claimKnowledge graph-based question-answering systems enable multi-hop question answering, allowing for the production of more complex and sophisticated answers by combining facts and concepts from knowledge graphs.

claimKnowledge graph-based question-answering systems facilitate the answering task by either using similarity measures or by producing structured queries in standard formats such as SPARQL.

referenceKnowledge graph-based question-answering systems, as researched by Singh et al. (2020) and Qiu et al. (2020), address the efficiency issues of traditional systems by employing structured data.

referenceHuang et al. (2019) proposed a knowledge graph embedding-based question-answering system (KEQA) that translates questions and answers into single triplets, such as (Leonardo, act, Inception), and represents the head entity, relation, and tail entity as a vector matrix in an embedding space.

claimIn knowledge graph-based question-answering systems, simple questions are answered by referring to a single triplet, while multi-hop questions require combining multiple entities and relations.

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 5 facts

claimDynamic reasoning systems for knowledge graph question answering include DRLK (Zhang M. et al., 2022), which extracts hierarchical QA context features, and QA-GNN (Yasunaga et al., 2021), which performs joint reasoning by scoring knowledge graph relevance and updating representations through graph neural networks.

claimKnowledge graph question answering (KGQA) systems leverage natural language processing techniques to transform natural language queries into structured graph queries.

procedureGeneration-retrieval frameworks for knowledge graph question answering, such as ChatKBQA (Luo H. et al., 2023) and GoG (Xu et al., 2024), use a two-stage approach that generates logical forms or new triples before retrieving relevant knowledge graph elements.

claimCurrent Knowledge Graph Question Answering (KGQA) systems frequently mishandle contextual continuity in multi-turn dialogues by either dropping or misapplying key constraints such as temporal filters.

claimReLMKG (Cao and Liu, 2023) employs graph neural networks (GNNs) for explicit knowledge propagation in knowledge graph question answering.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 4 facts

referenceCR-LT KGQA (Guo et al., 2024) is a Knowledge Graph Question Answering (KGQA) dataset that supports long-tail entities and commonsense reasoning.

referenceCoke, proposed by Dong et al. (2024a), minimizes LLM calls for Knowledge Graph Question Answering by using cluster-level Thompson sampling to formulate accuracy expectations and an optimized context-aware policy to select expert models based on question semantics.

referenceEXAQT (Jia et al., 2021) is a Knowledge Graph Question Answering (KGQA) dataset that supports temporal question answering with multiple entities, predicates, and conditions.

claimYu Zhang, Kehai Chen, Xuefeng Bai, Zhao Kang, Quanjiang Guo, and Min Zhang published the paper 'Question-guided knowledge graph re-scoring and injection for knowledge graph question answering' in 2024.

Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org arXiv Dec 4, 2025 3 facts

claimLatent graph representations often underperform compared to text-based methods on Knowledge Graph Question Answering (KGQA) tasks.

claimKnowledge Graph Question-Answering (KGQA) often requires integrating multiple reasoning steps that traverse the graph to connect related concepts.

claimStep-by-step interaction methods that allow iterative reasoning over graphs currently achieve the strongest results on Knowledge Graph Question Answering (KGQA) benchmarks.

Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org arXiv Jul 9, 2024 3 facts

referenceThe Right for Right Reasons (R3) methodology for Knowledge Graph Question Answering (KGQA) using LLMs treats common sense KGQA as a tree-structured search to utilize commonsense axioms, making the reasoning procedure verifiable.

referenceJinheon Baek, Alham Fikri Aji, and Amir Saffari authored the 2023 paper 'Knowledge-augmented language model prompting for zero-shot knowledge graph question answering' (arXiv:2306.04136).

claimSen et al. adopted an approach where facts from a KG are weighted by a Knowledge Graph Question Answering (KGQA) system before being fed into an LLM.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 2 facts

referenceJiang L and Usbeck R published 'Knowledge graph question answering datasets and their generalizability: are they enough for future research?' in the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval in 2022.

claimGrailQA is a benchmark for evaluating generalization in knowledge graph question answering.

A Knowledge Graph-Based Hallucination Benchmark for Evaluating ... arxiv.org arXiv Feb 23, 2026 1 fact

referenceKnowledge-Graph Question-Answer (KGQA) benchmarks use Knowledge Graphs, such as Wikidata (Vrandečić and Krötzsch, 2014) and DBpedia (Auer et al., 2007), to generate questions.

Empowering GraphRAG with Knowledge Filtering and Integration arxiv.org arXiv Mar 18, 2025 1 fact

claimThe researchers utilize the WebQSP and CWQ benchmark datasets for Knowledge Graph Question Answering (KGQA) tasks.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 1 fact

procedureThe KG-RAG pipeline operates by constructing a Knowledge Graph from unstructured text and subsequently performing information retrieval over that graph to execute Knowledge Graph Question Answering (KGQA).

Applying Large Language Models in Knowledge Graph-based ... arxiv.org Benedikt Reitemeyer, Hans-Georg Fill · arXiv Jan 7, 2025 1 fact

referenceHu, N., Wu, Y., Qi, G., Min, D., Chen, J., Pan, J.Z., and Ali, Z. conducted an empirical study of pre-trained language models in simple knowledge graph question answering, published in the journal World Wide Web in 2023.

Knowledge Graph Question Answering

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