The internet gives access to massive quantities of data in various formats; audio, image, text, and video; generated by humans or artificial intelligence. In this data-driven world, the rapid generation of information across various domains has challenged the reliability and trustworthiness of data. Ensuring the veracity of data has emerged as a fundamental concern for leveraging data effectively across different sectors. Traditional methods of ensuring information validity often fall short of handling the scale and complexity of modern data sources. However, the rise of knowledge graphs provides a promising solution to address the issue of data veracity. Knowledge graphs represent information through entities, relationships, and labels in a structured manner and provide a robust framework for managing, organizing, and validating datasets. This project explores the use of knowledge graph for data veracity as outlined in the paper entitled, “The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multidimensional Truth Finding. ” Specifically, this project focuses on the different construction methods of knowledge graphs and replicating the knowledge graph used in the above mentioned research paper.