Knowledge Base Question Answering
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Benchmarks
QALD-9-Plus
WebQuestionsSP
LC-QuAD 1.0
ComplexWebQuestions
SimpleQuestionsWikiData
GrailQA
SimpleQuestions
WebQSP
WebQSP-WD
WebQuestions
Most implemented
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Papers
SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter para…
Knowledge Base Question AnsweringInformation RetrievalSemantic ParsingDeSQ: Decomposition-based SPARQL Query Generation
Dominant approaches to Knowledge Base Question Answering (KBQA) fall into two categories. First is the generation of a formal query that suffers from brittleness and limited explainability, and the second is direct answe…
Knowledge Base Question AnsweringKG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-graph subgraph. Yet LLMs are known to halluc…
Knowledge Base Question AnsweringNode ClassificationGAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe knowledge-base feedback, and eventually return an answer. However, c…
Knowledge Base Question AnsweringReinforcement LearningDo Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategies fall into two paradigms based on plan…
Knowledge Base Question AnsweringKG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive reasoning. Structured Knowledge Graphs (KGs) provide an effective form of…
Knowledge Base Question AnsweringNatural Language UnderstandingReinforcement LearningKnowledge Graphs