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 text and its semantics.
A comparison between two data points may appear simple. However, we often encounter objects that contain multiple attributes and relationships. To be able to compare these objects, a more complex structure for comparison shall be created. Experts may also use individualized code for their comparison strategies.
The goal of this project is to develop a customizable execution framework that allows experts to write, execute, and manage custom code for fact comparison. This framework should support the integration of execution results and logging information into the system, ensuring transparency and traceability. By enabling dynamic code execution, the project aims to empower experts with the tools needed to handle complex fact comparison scenarios effectively.
Technologies: Python; Flask; Azure Cloud Services; Schema.org; REST; RabbitMQ
Tags: FactCheck; Framework; Design; Fact Comparison; Precision Metrics; Berger
