Exploratory Methods for Relation Discovery in Archival Data
This work addresses the need for archivists to enrich historical records with graph patterns, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of discovering relations in art historical archives by using exploratory data analysis and classification models to predict new relations, achieving higher precision for relations based on biographical information compared to other types.
In this article we propose a holistic approach to discover relations in art historical communities and enrich historians' biographies and archival descriptions with graph patterns relevant to art historiographic enquiry. We use exploratory data analysis to detect patterns, we select features, and we use them to evaluate classification models to predict new relations, to be recommended to archivists during the cataloguing phase. Results show that relations based on biographical information can be addressed with higher precision than relations based on research topics or institutional relations. Deterministic and a priori rules present better results than probabilistic methods.