Knowledge Graph Question Answering
Also known as KGQA
Overview
Knowledge Graph Question Answering (KGQA) connects questions to entities and relations in a graph, then returns answers supported by its structured facts. Common approaches translate questions into SPARQL or logical forms, retrieve and traverse multi-hop paths, or combine graph neural networks and symbolic rules with large language models and retrieval-augmented generation. Benchmarks such as WebQSP, ComplexWebQuestions, and GrailQA test query execution, compositional reasoning, and generalization, while newer evaluations examine temporal, biomedical, and incomplete-graph settings. A central challenge is ensuring that answers are both accurate and grounded in relevant evidence, especially when graphs are sparse or noisy and benchmark questions may not require genuine multi-hop reasoning.
Latest papers
169 papers20 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.