The new generations are born into a world where the internet is a natural extension of the real world. The online logs created throughout their lives might remain long after they are gone - detailed information about their everyday activity. Currently, corporations use this data to predict short-term actions in order to maximize the use of their services, which is but one of many use-cases that such an opportunity presents. Knowledge graphs, which have been the target of intensive research in recent years, were used in this work to model personal data. The project aims to create a framework for centralizing a person's logs originating from multiple sources on the web. Specifically, this work makes the following contributions: 1. Developed a framework to store a person's records into a usable and interpretable structure, providing a review of its possibilities and limitations with hopes of guiding future research. 2. Created a proof of concept made from a single user's data downloaded from five of the most widely used online platforms. 3. Performed experiments using pre-established models based on the concepts of metapaths, to explore the interactions between entities in the network and explore its semantic and structural value.