KGFusionX: Linking, Combining, and Exploring Data Through Knowledge Graphs

Abstract

In the realm of data exploration, the persistent challenges of data disconnection and inconsistency often hinder the efficiency of data analysts, especially in terms of data enrichment and aggregation. This thesis focuses on addressing the following research questions: How can we improve data integration and reuse of data in a clean and downloadable format to facilitate data analysis? Moreover, how can we contextually expand data on the fly to leverage its value and enhance data exploration? This work proposes KGFusionX, a knowledge graph centered framework that recognizes the time-intensive nature of data enrichment and integration. The study employs a backend implementation utilizing knowledge graphs to seamlessly connect disparate datasets. Several datasets from Lebanon covering different domains (e.g. health care, economy, education, and others) were converted and published as openly accessible knowledge graphs in a triple store repository (749,500 triples). This conversion allows efficient and fast aggregation of data because of the connections generated by knowledge graphs. Also, it is integrated with open linked data sources that serves as a resource to expand the data. The framework is showcased through an online platform built with Streamlit that allows users to select, combine, and download tabular data that can be used in other visualization exploration tools (e.g. PowerBI and Tableau). The approach was evaluated by data analysts and two use cases. Potential pickup of our platform was expressed by users who relied on the tool to analyze school and university challenges in rural areas, in addition to boosting tourism in Lebanon. The results demonstrated a significant improvement in data exploration efficiency, and better visuals with the knowledge graph-driven approach proving successful in overcoming the challenges posed by disconnection, inconsistency, and enrichment. This research primarily contributes to streamlining data exploration using the high potential of knowledge graphs to support data aggregation, data enrichment and visual data analysis.