Due to their expressive power, Knowledge Graphs (KGs) have received increasing interest not only as means to structure and integrate heterogeneous information but also as a native storage format for large amounts of knowledge and statistical data.
Therefore, analytical queries over KG data, typically stored as RDF, have become increasingly important.
Yet, formulating such queries represents a difficult task for users that are not familiar with the query language (typically SPARQL) and the structure of the dataset at hand.
To overcome this limitation, we propose Re2xOLAP: the first comprehensive interactive approach that allows to reverse-engineer and refine RDF exploratory OLAP queries over KGs containing statistical data.
Thus, Re2xOLAP enables to perform KG exploratory analytics without requiring the user to write any query at all.
We achieve this goal by first reverse-engineering analytical SPARQL queries from a small set of user-provided examples and then, given the reverse-engineered query, we propose intuitive and explainable exploratory query refinements to iteratively help the user obtain the desired information.
Our experiments on real-world large-scale KGs show that Re2xOLAP can efficiently reverse-engineer analytical SPARQL queries solely based on a small set of input examples.
Additionally, we demonstrate the expressive power of our interactive refinement methods by showing that Re2xOLAP allows users to navigate hundreds of thousands of different exploration paths with just a few interactions.
Pedersen, Torben Bach.
“Example-Driven Exploratory Analytics over Knowledge Graphs.”
Proceedings of the 26th International Conference on Extending Database Technology, EDBT 2023