Semantic Nexus: Knowledge Graph Creation from CrossNER Data using NLP

There is an abundance of raw text which contains plentiful information which could be classified into entities,relations and knowledge graphs. We could obtain this by using NLP approaches.The comprehensive CrossNER dataset,which contains Named Entity Recognition (NER) data from multiple areas, is used in this research study to offer a method for creating Knowledge Graphs(KGs). We especially employ NER, Relation Extraction (RE),and Knowledge Graph creation within the context of Artificial Intelligence from the CrossNER dataset.Our method uses this dataset to generate Knowledge Graphs,which provide an organized representation of the data. Crucial steps in the procedure include phrase segmentation, entity extraction, and relationship identification. This technique helps organize and extract knowledge for a variety of uses. The resulting Knowledge Graphs are priceless tools for knowledge management, improving information retrieval, and getting insights into certain domains.