Article: Intelligent Q&A model construction supported by natural language processing and knowledge graphs Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.41 pp.59 - 73 Abstract: This paper proposes an intelligent Q&A model that integrates natural language processing and knowledge graph technology. Aiming at the problem of insufficient depth of semantic understanding and weak knowledge relevance of traditional Q&A system, we adopt BERT-based semantic parsing model to realise intent recognition and entity extraction of user questions, and combine with Neo4j graph database to construct multi-source knowledge graph to realise structured knowledge storage; we realise dynamic matching between question vectors and knowledge subgraphs through graph neural network (GNN), and we design multi-jump inference mechanism to improve the ability of answering complex questions. Experiments on the open-domain Q&A dataset show that the model has an accuracy of 90.2% and a recall of 88.7%. The model is validated in educational counselling and medical Q&A scenarios, providing technical support for intelligent services in knowledge-intensive domains. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent Q&A model construction supported by natural language processing and knowledge graphs

Authors: Xiaoxia Yang

Addresses: School of Humanities, Communication University of China, Beijing 100024, China; School of Literature and Historical Culture, Dezhou University, Shandong Province 253023, China

Abstract: This paper proposes an intelligent Q&A model that integrates natural language processing and knowledge graph technology. Aiming at the problem of insufficient depth of semantic understanding and weak knowledge relevance of traditional Q&A system, we adopt BERT-based semantic parsing model to realise intent recognition and entity extraction of user questions, and combine with Neo4j graph database to construct multi-source knowledge graph to realise structured knowledge storage; we realise dynamic matching between question vectors and knowledge subgraphs through graph neural network (GNN), and we design multi-jump inference mechanism to improve the ability of answering complex questions. Experiments on the open-domain Q&A dataset show that the model has an accuracy of 90.2% and a recall of 88.7%. The model is validated in educational counselling and medical Q&A scenarios, providing technical support for intelligent services in knowledge-intensive domains.

Keywords: natural language processing; NLP; knowledge graph; intelligent question answering models; graph neural networks; GNNs.

DOI: 10.1504/IJICT.2025.149990

International Journal of Information and Communication Technology, 2025 Vol.26 No.41, pp.59 - 73

Received: 11 Aug 2025
Accepted: 05 Oct 2025
Published online: 20 Nov 2025 *

Title: Intelligent Q&A model construction supported by natural language processing and knowledge graphs

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