Knowledge Graph for Covid-19: a data fusion approach - Cerri, André; Nolasco, Diogo; Oliveira, Jonice (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
The Covid-19 pandemic generated a rapid and heterogeneous growth of scientific, technological, and social data, making it difficult for researchers and decision-makers to follow the available evidence in a timely manner. To address this problem, this paper presents the Knowledge Graph for Covid-19 (KGC-19), a knowledge graph designed to integrate information from PubMed articles, WIPO, EPO and LATIPAT patents, and X (formerly Twitter) posts. The proposed pipeline uses ETL scripts to collect, filter, standardize, and load the data into Neo4j according to a previously defined ontology. Named Entity Recognition models, including Flair and BioNER, are then applied to identify additional entities in texts and enrich the graph. The resulting graph connects scientific, technological, and social entities, enabling semantic queries and multidimensional analysis in the Covid-19 domain. KGC-19 therefore offers a promising approach to organize large volumes of unstructured pandemic-related data and support the discovery of relationships between research production, technological development, and social perception.