Modeling and Using Biographical Linked Data for Prosopographical Data Analysis

Biographical data is used for identifying people, groups, and organizations and for conveying information about them. Biographical data describes life stories of people with the aim of getting a better understanding of their personality, actions, and interperson relations. The underlying texts can also be used for data analysis and distant reading once the documents are provided in a machine-readable format. Prosopographical analysis delves into the life stories of individuals within a defined group to identify shared characteristics and patterns. This dissertation presents and utilizes a comprehensive framework for managing and analyzing biographical data in Digital Humanities research. It includes data models, methods, and applications that enrich biographical content with links and reasoning to enhance the findability, accessibility, interoperability, and re-usability following the FAIR principles. Furthermore, the framework includes versatile tools for both individual biographical research and prosopographical research on groups of people. Linked Data together with event-based data model schemas are used in the published datasets to achieve the interoperability of heterogeneous data regarding historical people. Events are used as the glue combining information from various sources. The event-based modeling enables depicting historical narratives as data, which can be further enriched with the events of individual people and organizations. The research included in this dissertation follows the principles of the design science and action research. The research has been carried out in multiple research projects concentrating on biographical data: WarSampo (2015–), BiographySampo (2018–2021), Norssi High School Alumni (2017), AcademySampo (2019–2021), LetterSampo (2020–2022), and ParliamentSampo (2021–). The data publications and services, online portals, and published articles with analysis are represented as the results of the work accomplished for this thesis. Besides, this thesis tackles the practices of creating, modeling, and publishing Linked Data, as well as analyzing this biographical and prosopographical data by the means of network and data analysis.