# Exploratory Methods for Relation Discovery in Archival Data

arXiv: https://arxiv.org/abs/2202.11361 · HTML: https://mlsanity.com/paper/2202.11361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017)
- [Enhancing semantic expressivity in the cultural heritage domain: exposing the Zeri Photo Archive as Linked Open Data](https://mlsanity.com/paper/1605.01188/) (2016)

## Abstract (arXiv)

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.