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
• ISSN: 0925-9902; 1573-7675
• DOI: 10.1007/s10844-023-00839-4

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 well-structured relationship between them in the KG. However, such strategies are difficult to build and train, limiting their consistency and versatility. The use of language models such as BERT has aided in the advancement of natural language question answering. In this paper, we present a novel Graph Neural Network(GNN) based approach with relevance scoring for improving KGQA. GNNs use the weight of nodes and edges to influence the information propagation while updating the node features in the network. The suggested method comprises subgraph construction, weighing of nodes and edges, and pruning processes to obtain meaningful answers. BERT-based GNN is used to build subgraph node embeddings. We tested the influence of weighting for both nodes and edges and observed that the system performs better for weighted graphs than unweighted graphs. Additionally, we experimented with several GNN convolutional layers and obtainined improved results by combining GENeralised Graph Convolution (GENConv) with node weights for simple questions. Extensive testing on benchmark datasets confirmed the effectiveness of the proposed model in comparison to state-of-the-art KGQA systems.