Papers Graph Question Answering
“Graph Question Answering” 태그가 달린 논문 140편 · 필터 해제
The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs
A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model what to do with them. We vary all four ov…
Graph Question AnsweringKnowledge GraphsQuery-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation
GNN-based Knowledge Graph Question Answering (KGQA) pipelines process queries through four discrete stages: entity linking, subgraph retrieval, GNN reasoning, and answer generation. Standard robustness evaluations confla…
Graph Question AnsweringAnswer GenerationEntity LinkingCompositional Chain-of-Relations for Faithful Knowledge Graph Question Answering with Large Language Models
Knowledge graph question answering (KGQA) is a key task for evaluating KG-augmented Large Language Models (LLMs), and complex KGQA that requires multi-hop reasoning is especially challenging. Solving a complex query invo…
Graph Question AnsweringSABET-QA: Temporal Knowledge Graph Question Answering
Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines. We pro…
Graph Question AnsweringKnowledge GraphsMissDiag: Diagnostic Evaluation of Incomplete-Knowledge Robustness in KGQA and KG-RAG
Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, an…
Graph Question AnsweringKnowledge GraphsMARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to-date and grounded information. Combinin…
Graph Question AnsweringKnowledge GraphsOntology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering
Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centered expansion, which faces two key challen…
Graph Question AnsweringWhen Correct Edges Cannot Be Verified: A Provenance Gap in Incomplete KGQA and a Provenance-Favoring Completion Policy
Incomplete Knowledge Graph Question Answering (IKGQA) requires completing missing edges to continue reasoning. A growing line of work verifies completed edges against retrieved text, treating textual support as a proxy f…
Graph Question AnsweringBounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering
LLM-based knowledge-graph question answering (KGQA) delegates graph traversal to language models, turning each question into a sequence of local relation-selection decisions repeated across beams and hops. A common but u…
Graph Question AnsweringText-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP
Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches often rely on large models or full supervision in the form of gold q…
Graph Question AnsweringReinforcement LearningKnowledge GraphsCommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models
To effectively interact with the real world, Large Language Models (LLMs) require entity-based commonsense reasoning, a challenging task that necessitates integrating factual knowledge about specific entities with common…
Graph Question AnsweringPathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering
Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the retrieval-augmented generation paradigm to ground Large Language Mod…
Graph Question AnsweringKnowledge GraphsHOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities
Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured knowledge. Integrating these two in a complementary manner enables the de…
Graph Question AnsweringKnowledge GraphsConformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
Knowledge Graph Question Answering (KGQA) offers grounded, interpretable reasoning, but existing methods often fail to provide reliable coverage guarantees over retrieved answers. While Conformal Prediction (CP) offers a…
Graph Question AnsweringKnowledge GraphsSCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering
Large language models excel at complex reasoning, yet evaluating their intermediate steps remains challenging. Although process reward models provide step-wise supervision, they often suffer from a risk compensation effe…
Graph Question AnsweringCacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering
The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answering (KGQA). However, existing LLM-driven KGQA systems act as stateless p…
Graph Question AnsweringSemantic ParsingWhich bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement
Large language models still struggle with faithfulness and hallucinations despite their remarkable reasoning abilities. In Knowledge Graph Question Answering (KGQA), semantic parsing-based approaches address the limitati…
Graph Question AnsweringSemantic ParsingTRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering
Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning step independently and fail to effectively leverage experience from…
Graph Question AnsweringGraph Query Generation with Constraint-guided Large Language Agents
Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and property graphs underexplored, despite increasing demand for unified KGQA…
Graph Question AnsweringIs One Token All It Takes? Graph Pooling Tokens for LLM-based GraphQA
The integration of Graph Neural Networks (GNNs) with Large Language Models (LLMs) has emerged as a promising paradigm for Graph Question Answering (GraphQA). However, effective methods for encoding complex structural inf…
Graph Question Answering