Globally, Twitter, now known as X, is the 3rd most popular Online Social Network (OSN), behind Facebook and Instagram. Its data model and data access API are simpler than those of other OSNs. This makes it perfect for social network studies that aim to examine the structure of the social graph, the sentiment towards different entities, the patterns of online behavior, and the types of malicious attacks in a vibrant network with hundreds of millions of members. Over the past ten years, Twitter has been used in over 10,000 research articles, establishing it as a significant research platform. There are few attempts to map this research landscape overall, despite the fact that the majority of the research that uses Twitter has outstanding evaluation and comparative studies. Using data from Twitter, this study attempts to create a knowledge graph (KG) of ransomware attacks (RA) and investigate the difficulties related to ransomware knowledge. Ransomware is a worldwide threat that is constantly changing. Three essential processes are involved in creating a KG from unstructured text: gathering and cleaning data, extracting entities, and extracting relationships. This work extracts ransomware entities from unstructured data using a previously suggested ransomware ontology, customizing it to match attacks that have been reported on Twitter. The KG is created by identifying the relationships between the entities that have been extracted. A tracing technique is used to assess the accuracy of the generated knowledge graph to demonstrate its efficiency. The proposed method has surprisingly achieved high accuracy when compared to relevant studies, with an accuracy metric of 93.42 and an F1-score of 94.03.