Linking archival data to location A case study at the UK National Archives

Clough, Paul; Tang, Jaiyu; Hall, Mark M. and Warner, Amy (2011). Linking archival data to location A case study at the UK National Archives. Aslib Proceedings, 63(2/3) pp. 127–147.



The National Archives (TNA) is the UK Government's official archive. It stores and maintains records spanning over a 1,000 years in both physical and digital form. Much of the information held by TNA includes references to place and frequently user queries to TNA's online catalogue involve searches for location. The purpose of this paper is to illustrate how TNA have extracted the geographic references in their historic data to improve access to the archives.

To be able to quickly enhance the existing archival data with geographic information, existing technologies from Natural Language Processing (NLP) and Geographical Information Retrieval (GIR) have been utilised and adapted to historical archives.

Enhancing the archival records with geographic information has enabled TNA to quickly develop a number of case studies highlighting how geographic information can improve access to large‐scale archival collections. The use of existing methods from the GIR domain and technologies, such as OpenLayers, enabled one to quickly implement this process in a way that is easily transferable to other institutions.

Practical implications
The methods and technologies described in this paper can be adapted, by other archives, to similarly enhance access to their historic data. Also the data‐sharing methods described can be used to enable the integration of knowledge held at different archival institutions.

Place is one of the core dimensions for TNA's archival data. Many of the records which are held make reference to place data (wills, legislation, court cases), and approximately one fifth of users' searches involve place names. However, there are still a number of open questions regarding the adaptation of existing GIR methods to the history domain. This paper presents an overview over available GIR methods and the challenges in applying them to historical data.

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