Knowledge graphs for NLP: A comprehensive analysis

Abstract

Comprehensive analysis done for this paper examines the blend of knowledge graphs (KGs) and natural language processing (NLP), emphasizing the collective potential of both techniques to improve understanding and processing of textual data amid its rapid growth. KGs provide structured semantic representations that facilitate deeper reasoning and contextual understanding, addressing the limitations inherent in traditional NLP approaches. By consolidating insights from over 79 research papers, the review in-depth explores the definitions, applications, and challenges related to the integration of KGs and NLP, as well as their synergistic applications in multiple domains, such as question answering, sentiment analysis, and text summarization. The review underscores the transformative impact of KGs in bridging unstructured text with structured data, paving the way for innovative methodologies in AI applications. Additionally, it identifies prevailing challenges in the construction and management of KGs while emphasizing the ongoing evolution and promising future of this integrated approach in tackling real-world NLP challenges. The findings aim to benefit both researchers and practitioners in the field, promoting the adoption of KG-based methods across diverse applications.