KGQAn: A Universal Question-Answering Platform for Knowledge Graphs

KGQAn aims to develop a data science chatbot that can answer questions from an arbitrary KG without prior knowledge of the KG. KGQAn proposes a novel formalization of question understanding as a triple pattern extraction modelled using a Seq2Seq neural network. Our model generalizes to understand questions across diverse domains. Moreover, KGQAn introduces a just-in-time linking and filtering approach, which performs entity and relation linking as semantic search queries partially offloaded to the RDF engines without requiring any pre-processing. Thus, KGQAn acts as an on-demand KG question-answering service.