Modest doubt: enabling discovery across maritime heritage records.
Jack Pink1, Shrikriti Singh2, Adriane Chapman2
1Environmental Social Sciences and Stanford Robotics Center, Stanford University, Stanford, CA United States of America.
International Journal of Digital Humanities
|July 16, 2026
Summary
This study introduces a new method using Few Shot learning and Multiclass Text Classification to organize fragmented UK maritime heritage records. This approach enhances data accessibility for archaeological research.
Area of Science:
- Archaeology
- Data Science
- Heritage Management
Background:
- UK maritime heritage records are vital but fragmented, hindering research synthesis.
- Legacy data and varied recording methods complicate heritage information access.
- Current archaeological research requires efficient methods for analyzing disparate heritage datasets.
Purpose of the Study:
- To explore and test a method for enhancing and enriching fragmented UK maritime heritage datasets.
- To apply Few Shot learning techniques for Multiclass Text Classification on heritage records.
- To improve the organization and searchability of the Welsh National Monuments Record.
Main Methods:
- Utilizing Named Entity Recognition (NER) and Natural Language Processing (NLP) tools.
- Employing Few Shot learning for Multiclass Text Classification.
- Testing the approach on the Welsh National Monuments Record (NMR).
Main Results:
- Demonstrated the potential of Few Shot learning to classify and organize unstructured heritage data.
- Showcased a method to enhance fragmented maritime heritage records for better synthesis.
- Provided a framework for improving accessibility of archaeological context information.
Conclusions:
- Few Shot learning offers a promising solution for organizing and analyzing complex, fragmented heritage datasets.
- The developed method can significantly improve the efficiency of archaeological research on maritime heritage.
- Enhanced data accessibility from the Welsh NMR can support future heritage management and research priorities.
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