Knowledge Graphs and Semantic Understanding in Natural Language Processing

Dear Colleagues,

Knowledge graphs (KGs) and semantic understanding are fundamental to advancing natural language processing (NLP), enabling machines to interpret context, relationships, and meaning more effectively. KGs provide structured representations of entities and their interconnections, significantly enhancing search engines, chatbots, and question-answering systems by linking queries to relevant knowledge. Meanwhile, semantic understanding, powered by techniques like word embeddings and transformer models, allows machines to capture implicit meanings in text, leading to breakthroughs in searches, recommendation systems, sentiment analysis, and specialized domains like healthcare, finance, and others.

This Special Issue will contribute in the future of NLP, which is set to be transformed by the deeper integration of KGs with large language models (LLMs), enabling more advanced reasoning, the improved explainability of AI-driven decisions, and more efficient real-time data processing. Emerging trends include self-evolving KGs that dynamically update with new information; multimodal knowledge integration that fuses text, images, and structured data; and hybrid AI approaches that combine symbolic reasoning with deep learning

Dr. Wajahat Ali Khan
Guest Editor

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