A novel technique using graph neural networks and relevance scoring to improve the performance of knowledge graph-based question answering systems

• Published in: Journal of intelligent information systems Vol. 62; no. 3; pp. 809 - 832
• Main Authors: Thambi, Sincy V., Reghu Raj, P. C.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Data Structures and Information Theory; Graph neural networks; Graph theory; Graphs; Information Storage and Retrieval; IT in Business; Knowledge representation; Natural language; Natural Language Processing (NLP); Neural networks; Nodes; Questions; System effectiveness; Relevance scoring; Question answering; Graph embedding; Graph neural network; Knowledge graph

A Knowledge Graph-based Question Answering (KGQA) system attempts to answer a given natural language question using a knowledge graph (KG) rather than from text data. The current KGQA methods attempt to determine whether there is an explicit relationship between the entities in the question and a we...