TrustFuse: A Fusion Testbed for Uncertain Knowledge Reconciliation

To build a knowledge graph, knowledge can be extracted from multiple data sources. However, for a given topic, multiple data sources rarely provide a unified view of the data. The data may differ in unit scales, levels of specificity, or even be contradictory. To jointly find the most trustworthy data and evaluate the reliability of the sources, data fusion approaches are usually applied. Although existing tools implement such approaches, they often lack essential functionalities such as a template for developing data fusion approaches, evaluation metrics, or a user-friendly visualization of the fused results. To overcome these limitations, we introduce TrustFuse, a comprehensive testbed that supports experimentation with fusion models, their evaluation, and the visualization of datasets as graphs or tables within a unified user interface.