Knowledge Graph Enhanced Natural Language Understanding for Intelligent Question Answering Systems
Keywords:
knowledge graph, natural language understanding, question answering, neural-symbolic integration, semantic reasoningAbstract
Intelligent question answering systems require a deep, structured understanding of natural language that extends beyond surface-level pattern recognition. This paper examines the integration of knowledge graphs with natural language understanding architectures to develop robust, context-aware question answering platforms. We argue that knowledge graphs provide a formal, relational representation of entities and their semantic connections, which enhances the ability of neural models to perform reasoning, disambiguation, and multi-hop inference. By embedding structured knowledge into transformer-based language models, these hybrid systems overcome limitations of purely statistical approaches, such as handling rare entities, temporal constraints, and compositional queries. The paper systematically explores architectural trade-offs, including the choice of graph embedding techniques, the granularity of entity linking, and the balance between symbolic reasoning and differentiable learning. We also address governance challenges related to fairness, bias propagation through graph structures, and the sustainability of large-scale knowledge graph maintenance. Deployment considerations such as latency, scalability, and integration with existing enterprise infrastructures are analyzed through cross-domain case illustrations from biomedicine, e-commerce, and legal analytics. Finally, we propose forward-looking perspectives on policy implications and the need for transparent, explainable question answering mechanisms that respect user privacy and data sovereignty. The findings suggest that knowledge graph enhanced natural language understanding represents a critical evolution in the pursuit of intelligent, trustworthy question answering systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
