TrustFuse: A Fusion Testbed for Uncertain Knowledge Reconciliation
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Lucas Jarnac, Yoan Chabot, Miguel Couceiro
Institutions: Centre National de la Recherche Scientifique, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Université de Lorraine, Orange (France), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, University of Lisbon, Instituto Superior Técnico
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4417148568