Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks

Frontiers in Research Metrics and Analytics

Andrew W. Bartlow¹  Jeanne M. Fair¹  John Ambrosiano²  William Rosenberger²  Mark Ressler²  Benjamin Sims³ 

[1] Biosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, United States;Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, United States;Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United States;

关键词: collaboration;    connectional intelligence;    bioinformatics;    global health;    ontology;    social network;   

DOI  :  10.3389/frma.2020.00003

来源: DOAJ

【 摘 要 】

Scientific research communities can be represented as heterogeneous or multidimensional networks encompassing multiple types of entities and relationships. These networks might include researchers, institutions, meetings, and publications, connected by relationships like authorship, employment, and attendance. We describe a method for efficiently and flexibly capturing, storing, and extracting information from multidimensional scientific networks using a graph database. The database structure is based on an ontology that captures allowable types of entities and relationships. This allows us to construct a variety of projections of the underlying multidimensional graph through database queries to answer specific research questions. We demonstrate this process through a study of the U.S. Biological Threat Reduction Program (BTRP), which seeks to develop Threat Reduction Networks to build and strengthen a sustainable international community of biosecurity, biosafety, and biosurveillance experts to address shared biological threat reduction challenges. Networks like these create connectional intelligence among researchers and institutions around the world, and are central to the concept of cooperative threat reduction. Our analysis focuses on a series of seven BTRP genome sequencing training workshops, showing how they created a growing network of participants and countries over time, which is also reflected in coauthorship relationships among attendees. By capturing concept and relationship hierarchies, our ontology-based approach allows us to pose general or specific questions about networks within the same framework. This approach can be applied to other research communities or multidimensional social networks to capture, analyze, and visualize different types of interactions and how they change over time.

【 授权许可】

Unknown