Medical Knowledge Graphs in the Discovery of Future Research Collaborations

Abstract

AbstractThis chapter introduces a framework that is based on a novel graph-based text representation method and combines graph-based feature selection, text categorization and link prediction to advance the discovery of future research collaborations. Our approach integrates into a single knowledge graph both structured and unstructured textual data through a novel representation of multiple scientific documents. The Neo4j graph database is used for the representation of the proposed scientific knowledge graph. For the implementation of our approach, we use the Python programming language and the scikit-learn machine learning library. We assess our approach against classical link prediction algorithms using accuracy, recall and precision as our performance metrics. Our experiments achieve state-of-the-art accuracy in the task of predicting future research collaborations. The experimentations reported in this chapter use the COVID-19 Open Research Dataset.KeywordsLink predictionText categorizationFeature selectionKnowledge graphsNatural language processingDocument representation