Analyzing Induced Functional Dependencies from Spreadsheets in the GF Framework for Ontology-Based Data Access

Expressing information from spreadsheets as relational databases is crucial in the context of ontology-based data access as it facilitates structured data management and lays the foundation to transform the data into ontologies to attain advanced data processing techniques, such as reasoning and inferencing. We extend the GF framework for Ontology-Based Data Access with functionality for determining and revising the functional dependencies that are held in a spreadsheet. An initial set of tentative functional dependencies is computed using the TANE data mining algorithm. This set can then be revised by the user who is considered an oracle. Given a functional dependency, the user can see the tuples from the spreadsheet justifying it and can revise the validity of the functional dependency with the help of our system. This process is carried out by generating tuples not present in the dataset by using values already present in the table, so the user can check their prospective viability. We present a running example along with a downloadable JAVA-based application with the source code of the miner in the C programming language and the files used in our experiments to help with the reproducibility of our results. We also apply the approach to a real dataset comprised of the catalog of the library of the Department of Computer Science at Universidad Nacional del Sur.