As an academic domain matures, the analysis of information becomes increasingly more complex and arduous due to the accelerated volume of published articles and new technologies that are developed, making the sources of information available more scattered. With the advance of the internet, the realisation of research has become more accessible, while at the same time resulting in more information to be analysed. Combining the numerous sources of new information with the staggering rhythm with which open-source technologies are developed, there is an increased difficulty in keeping up with all of the new advancements in a specific domain. The utilisation of natural language processing (NLP) tools enables the construction of knowledge graphs for a domain of information, containing its entities and connecting them through their relations. This way the information becomes available in a more organised and interconnected manner, making it easier for research and development to be accomplished. This project aims to utilize existing tools to extract entities and relations from a corpus of text files related to the blockchain field, a relatively new and innovative technology, where new projects and concepts are constantly being introduced. An analysis of the results obtained was undertaken, to identify if they were well suited for the construction of a blockchain knowledge graph. A website was developed in order to visualize the knowledge graph. The tools that were developed for this project will be made available in a public repository, and the author encourages their use along with the prediction models used during this project in order to build knowledge graphs for any domain related to computer science, and not only blockchain.