Ontology-based information extraction from legacy surveillance reports of infectious diseases in animals and humans

Abstract: More and more institutes and health agencies choose knowledge graphs over traditional relational databases to store semantic data. The knowledge graphs, using some form of ontology as a framework, can store domain-specific information and derive new knowledge using a reasoner. However, much of the data that must be moved to the graphs is either inside a relational database, or inside a semi-structured report. While there has been much progress in developing tools that export data from relational databases to graphs, there is a lack of progress in semantic extraction from domain-specific unstructured texts. In this thesis, a system architecture is proposed for semantic extraction from semi-structured legacy surveillance reports of infectious diseases in animals and humans in Sweden. The results were mostly positive since the system could identify 17 out of the 20 different types of relations.