31 January 2018
Linking Privy Council Appeals Data
This post continues a series of blog posts relating to a PhD placement project that seeks to make data about appeals heard by the Judicial Committee of the Privy Council (JCPC) available in new formats, to enhance discoverability, and to increase the potential for new historical and socio-legal research questions. Previous posts looked at the historical context of the JCPC and related online resources, as well as the process of cleaning the data and producing some initial visualisations.
When looking at the metadata about JCPC judgments between 1860 and 1998, it became clear to me that what was in fact being represented here was a network of appeals, judgments, courts, people, organisations and places. Holding this information in a spreadsheet can be extremely useful, as demonstrated by the visualisations created previously; however, this format does not accurately capture the sometimes complex relationships underlying these cases. As such, I felt that a network might be a more representative way of structuring the data, based on a Linked Data model.
Linked Data was first introduced by Tim Berners-Lee in 2006. It comprises a set of tools and techniques for connecting datasets based on features they have in common in a format that can be understood by computers. Structuring data in this way can have huge benefits for Humanities research, and has already been used in many projects – examples include linking ancient and historical texts based on the places mentioned within them (Pelagios) and bringing together information about people’s experiences of listening to music (Listening Experience Database). I decided to convert the JCPC data to Linked Data to make relationships between the entities contained within the dataset more apparent, as well as link to external sources, where available, to provide additional context to the judgment documents.
The image below shows how the fields from the JCPC spreadsheet might relate to each other in a Linked Data structure.
In this diagram:
- Blue nodes represent distinct entities (specific instances of e.g. Judgment, Appellant, Location)
- Purple nodes represent the classes that define these entities, i.e. what type of entity each blue node is (terms that represent the concepts of e.g. Judgment, Appellant, Location)
- Green nodes represent properties that describe those entities (e.g. ‘is’, ‘has title’, ‘has date’)
- Orange nodes represent the values of those properties (e.g. Appellant Name, Judgment Date, City)
- Red nodes represent links to external sources that describe that entity
Using this network structure, I converted the JCPC data to Linked Data; the conversion process is outlined in detail in the next blog post in this series.
A major advantage of converting the JCPC data to Linked Data is the potential it provides for integration with other sources. This means that search queries can be conducted and visualisations can be produced that use the JCPC data in combination with one or more other datasets, such as those relating to a similar historical period, geographical area(s), or subject. Rather than these datasets existing in isolation from each other, connecting them could fill in gaps in the information and highlight new relationships involving appeals, judgments, locations or the parties involved. This could open up the possibilities for new research questions in legal history and beyond.
Linking the JCPC data will also allow new types of visualisation to be created, either by connecting it to other datasets, or on its own. One option is network visualisations, where the data is filtered based on various search criteria (e.g. by location, time period or names of people/organisations) and the results are displayed using the network structure shown above. Looking at the data as a network can demonstrate at a glance how the different components relate to each other, and could indicate interesting avenues for future research. In a later post in this series, I’ll look at some network visualisations created from the linked JCPC data, as well as what we can (and can’t) learn from them.
This post is by Sarah Middle, a PhD placement student at the British Library researching the appeal cases heard by the Judicial Committee of the Privy Council (JCPC). Sarah is on twitter as @digitalshrew.