Unlocking the potential of entity-centric knowledge graphs: transforming healthcare and beyond

Knowledge graphs (KGs) have become a cornerstone in organizing and utilizing information across various domains, from enhancing search engines to improving recommendation systems. KGs comprise nodes (entities) and edges (relations) that depict the knowledge within a specific field or a collection of domains. The potential of KGs to enable intricate reasoning and inference has been investigated across various endeavors, encompassing tasks such as information retrieval, and knowledge discovery. While KGs have come a long way, representing knowledge effectively remains a formidable challenge, especially in complex fields like healthcare and biomedicine. This article highlights our recent publication Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare (presented at K-CAP 2023) and explores the concept of entity-centric knowledge graphs, a relatively uncharted territory in the KG landscape, but one that holds immense promise in reshaping how we organize, access, and leverage data.