Knowledge graph-based question answering (KGQA) systems face several challenges. These include the need for detailed training data, difficulty in handling complex multi-hop queries, and dense knowledge gap interactions. The model needs training on annotated entities and relations, which requires significant human effort and time. We developed methodologies that improve end-to-end question answering with knowledge graphs, eliminating the need for pre-annotated entities (gold entities). Our approach incorporates language models—Text-to-Text Transformer (T5) and Longformer—and employs a named entity disambiguation technique. We reduced the dependency on gold entities by first removing explicit entity annotations from the training data and then augmenting this data with relevant knowledge base facts. In this paper, we explored two different methodologies: (1) training T5 and Longformer on this augmented dataset to answer factoid questions using inferred knowledge graph entities, and (2) applying transfer learning with SPARQL-based supervision to improve generalization. The experimental results demonstrate that the proposed models are efficient and offer effective strategies for addressing complex questions while significantly reducing the need for manual annotation of training data.
Kumari et al. (Wed,) studied this question.