This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically acquired knowledge from Web 3.0. With advanced methods of term and category extraction, classification using both lightweight machine learning and strong deep learning methods, and clustering, the framework can exceed all similar frameworks in terms of performance. Initial terms are processed from the documents using the TF-IDF strategy. Static metadata stack is established from e-books, query logs, and glossaries. Renyi entropy is used to guide informative term selection for a decision tree classification; in this manner, the scale of the generated knowledge is managed by lightweight computing requirements. Dynamic knowledge from Web 3.0 is classified using GANs and is subjected to DBSCAN clustering. A multi-agent setup with parallelization enhances the efficiency of the clustering process. The knowledge graph is finally created using the Co-SIM rank and is evaluated for specificity using Hellinger's distance and volume of intersection index. These paradigms contribute to this framework significantly outperforming the baseline frameworks.