Building Knowledge Graphs for Data Analysis Using Embedding and Indexing Techniques: An Agile Methodology

Decision-making requires comprehensive and accurate information from diverse sources to ensure well-informed, data-driven decisions. Knowledge Graphs (KGs) play a significant role in this context, as they can integrate and represent data in a structured manner, allowing for a deeper understanding of the data. KGs provide semantic indicators that help discover new facts and hidden patterns that support decision-making. In this paper, we present a methodology for building a knowledge graph from distributed data using embedding and semantic indexing techniques. We adhered to agile and cost-effective principles in selecting the models and methods used to implement the methodology. We conducted testing and evaluation of the methodology's outputs, and the results were satisfactory.