FactCheck is a framework for detecting and resolving conflicting data on the Web. It establishes an entire fact comparison process that consists of data acquisition, data comparison, the presentation of comparison results, and comprehensive analysis functions. FactCheck is a leading research topic of our research group and bears challenges in many aspects.
We define facts as pieces of information that are published by data providers (e.g., as textual content in their website(s)). If two or more websites publish data on the same topic, we humans can compare the data critically. However, this task is quite difficult for a machine, as they do not have an inherent understanding of semantics.
A comparison between data points may appear simple. However, multiple functions may handle such a comparison at a time. Depending on the function design (and parameters), the yielded result may differ strongly. E.g., a comparison function focused on date comparison may return a boolean value (true/false) if the compared dates are the same, or the number of days in between the two dates.
The goal of this project is to develop a customizable comparison logic that allows experts to express how facts should be compared through formulas. These formulas shall orchestrate comparison functions using several operations (boolean, algebraic), and the system should validate the created formulas along multiple dimensions (syntax, semantics, safety). The final formulas should then be able to be stored in a database and executed on demand.
To ensure a user-friendly creation process, the expert user shall be informed about potential issues during the formula generation, and issues during the final validation steps shall be logged by the system, ensuring transparency and traceability of the process. Using this customizable comparison function logic, the project aims to empower experts with the tools needed to handle complex fact comparison scenarios effectively.
Technologies: Python; MongoDB; Docker; Web Applications; Web Services
Tags: FactCheck; Semantic Web; Fact Comparison; Precision Metrics; Berger
