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The Belgian Historical Gazetteer: an index of Belgian historical place names to link archival collections and explore landscapes histories

Legend: extracts from various sheets of the ‘reduced cadastre’ (©IGN/NGI)

Gazetteers can help historians with mapping toponyms that appear in the sources they analyse by providing them with lists of historical place names and extra information which can be used to disambiguate the latter, if it happens that several different places share a same name. This is the aim of initiatives such as the World Historical Gazetteer or Pleiades. However, despite their merits, the international scope of these tools impedes the use for regional and local historical research. Historians thus need more suitable gazetteers if they want to work at a more local scale.

CLARIAH-VL aims to fill in this gap for Belgium by launching the project “Belgian Historical Gazetteer” in the framework of its SIC “Enrich”. Started in November 2022, the pilot of this project will last two years, and eventually aims to cover the whole country in the coming years. It is hosted at the University of Antwerp and undertaken in liaison with the Belgian National Geographical Institute (IGN/NGI). The aim of this project is to set up a historical gazetteer of toponyms for the whole present-day territory of Belgium, in order to provide researchers with a collection of data that 1) does not stop at Belgian provincial borders 2) goes beyond the level of municipalities and 3) goes back in time as much as possible.

Pilot test

The first phase of the project (pilot 2022/2024) consists in the gathering of an ancient and geographically homogeneous set of toponyms for provinces Antwerp, East Flanders and Liège. This is done by collecting all the toponyms mentioned on the ‘reduced cadastre’, a reduced version of the primitive cadastre drawn between 1847 and 1855 to prepare the creation of the first topographical map of Belgium in the second half of the 19th century.

Workflow of the ‘reduced cadastre’ (©IGN/NGI)
Workflow of the ‘reduced cadastre’ (©IGN/NGI)

From QGis to Linked Open Data

These toponyms are located by using points in QGis and described via the PostGis extension in a PostgreSQL database whose structure is highly adaptable to every source and compatible with Linked Open Data principles. For each toponym we record not only its name, location, type (as described in the source, and a standardized version of it), but also if applicable its matching number in databases of other projects (for instance the “placename” project of the State Archives, the “Dorpskernen” project for Flemish toponyms), its corresponding Wikidata page if it already exists and its corresponding notice in the book Gemeenten van Belgïe[1], where Hervé Hasquin and his team summarized historical information (notably administrative and ecclesiastical belonging during the Ancien Régime) about municipalities.

The ultimate aim of drawing those links with the existing (notably historical) resources is to provide researchers with sufficient information on each toponyms to facilitate the disambiguation of place names. For instance, if one historian wants to locate a place called “heikant” in the primary source and if it appears that there are several “heikant” in Belgium, it may help him/her to identify the correct one among the latter if he knows in which bishopric each of them belongs in the seventeenth century. Finally, thanks to another table of the database (table “relations”), we also describe elementary relations between the toponyms like the administrative ones (“this hamlet belongs to this town” / “this isolated chapel belongs to this town”).

Future integration and accessibility

At the end of the project, this gazetteer will be published on the World Historical Gazetteer, and will progressively be loaded on Druid (a LOD platform), in order to make it easily reachable for everyone as well as potentially extendable. This gazetteer will enable researchers to easily identify place names they find in their sources, and could be used in the future to automatically ‘map’ (e.g. annotated) written sources to a great extent.

Beyond these very practical benefits, we think that this Belgian gazetteer can also be used to open new perspectives in landscape history by making it possible to follow the evolution of the Belgian landscape through the evolution of toponyms (name and location) on the long-term. This is why, in a second phase (which in practice is ran parallel to the first), we compare the nineteenth century toponyms with present-day ones using the database of the IGN/NGI. By establishing links between ancient and actual toponyms, we can explore how a toponym evolved during the nineteenth and the twentieth century in order to research variations of place names through time. In a third phase, toponyms extracted from older material will be added to the database and the same sort of links will be described, in order to make it possible to follow the evolution of place name on a longer term.


[1] Duvosquel, Jean-Marie, Hervé Hasquin, and Raymond Van Uytven. Gemeenten Van België: Geschiedkundig En Administratief-Geografisch Woordenboek. Bruxelles: La Renaissance du Livre, 1980.

Styloscope and Toposcope: Towards User-Friendly Digital Text Analysis

Natural Language Processing (NLP) has been one of the fastest-growing research fields in the last decade. Innovations such as pre-trained large language models based on transformer neural networks have not only led to the popularization of AI and NLP in the general public, but also to interdisciplinary research projects in the humanities and social sciences facilitated by the scalability of these methods. In this post, we present two tools that aim to facilitate said interdisciplinary research: Styloscope and Toposcope.  The tools were developed in Python and can be used from the command line or from a user interface. The code, detailed installation instructions, and user guidelines can be found on GitHub:

Styloscope

Styloscope is a tool for automatic writing style analysis. It can be used to test hypotheses about large-scale corpora, parse documents, or detect outliers. Users can provide data by either uploading a local file or by using a publicly available Huggingface dataset. When uploading a corpus, the tool accepts CSV files with one document per row, and ZIP folders in which documents are stored in individual text files. The output contains the parsed documents, raw statistics on various writing style features such as syntactic dependencies, lexical richness,  readability, etc., and visualizations of aggregated results. An example for syntactic dependencies is provided below:

Toposcope

Toposcope can be used to detect topics in unstructured text data. It provides annotations and visualizations of the detected topics, including (changes in) topic frequency over time. The tool features four algorithms: BERTopic (Grootendorst, 2022), Top2Vec (Angelov, 2020), Non-negative Matrix Factorization (Choo et al., 2013), and Latent Dirichlet Allocation (Blei et al., 2003). Users can modify a selection of topic model parameters, and apply a number of built-in preprocessing steps, such as lemmatization and stopword removal. The input format is identical to the Styloscope format: users can upload a local corpus (CSV/ZIP), or use a Huggingface dataset. The output includes visualizations of the topic-document clusters (as shown below) and the most important keywords per topic. The raw results, among other things, consist of annotations, a topic-document matrix, and a topic-term matrix. Topic diversity and topic coherence are also computed in order to support the user during the evaluation of the tool.

How to cite

Jens Lemmens and Walter Daelemans. 2024. Styloscope and Toposcope: Towards user-friendly digital text analysis. CLiPS Technical Report Series (CTRS): 10. https://www.uantwerpen.be/en/research-groups/clips/research/computational-linguistics/compling-resources/clips-technical-repo/

References

  • Dimo Angelov. 2020. Top2Vec: Distributed representation of topics. arXiv:2008.09470.
  • David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, vol. 3, pp. 993—1022.
  • Jaegul Choo, Changhyun Lee, Chandan K. Reddy and Haesun Park. 2013. Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Transactions on Visualization and Computer Graphics, vol. 19 (12), pp. 1992—2001. Institute of Electrical and Electronics Engineers (IEEE).
  • Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794.

CLARIAH-VL SIC 5 tool descriptions

A CLARIAH-VL supported data management system: nodegoat

Information on people, places, and things are related to each other in different ways. There are countless ways we can infer these relations which are research question and source dependent. There are also many ways to store this information as data. The nodegoat platform is an object oriented relational web-based data management system which also provides network and geospatial visualizations in one platform. It has the availability to develop custom data models, and then collaborate on this data and later generate visualizations and export data. Within CLARIAH-VL nodegoat is used to store, organize, maintain and analyze relational data. Below you can find the cases where the platform is currently or has been used.

To support researchers in their use of such data, CLARIAH-VL & GhentCDH are hosting 2 nodegoat workshops. In the workshop you will learn how to use this platform for your research. The workshops will be held in two parts on the same day:

1) beginners

2) advanced users (users that already are using nodegoat or have setup an instance).

The workshops, given by the developers of the platform – Lab1100, will be held on 16 November from 10 – 12h & 14 – 16h respectively at Ghent University. Both workshops will be given in English, but questions may be asked in Dutch. The exact location will be confirmed two weeks before the location to registered participants. Please register here: https://event.ugent.be/registration/nodegoat.

An example of the nodegoat instance, as used in the TIC Collaborative project. This network represents a social visualisation of people and conferences.

To get an idea of how nodegoat can be used in research, see this list of the use cases, all projects supported by GhentCDH (CLARIAH-VL):

Launch of DigHimapper, a platform to analyse historical maps via georeferencing and annotating

CLARIAH-VL is proud to announce the release of DigHimapper, a platform to analyse historical maps via georeferencing and annotating. It involves the public in two important steps in the processing of historical maps into analysable sources of information to make it possible to visualise landscape evolutions and search maps for place names.

  • In the Georeferencing portal, historical maps are placed as closely as possible to their current state. This is done by looking for points that can be found on both a historical map and in the current landscape. By repositioning the maps, it becomes possible to compare them directly with the present, as well as with other historical maps, thus visualising landscape evolutions.
  • In the Annotation portal, place names (toponyms) on historical maps are converted into text that can be read by computers. Combined with the repositioning of the map itself, these toponyms are given a place in space. Since toponyms often contain a wealth of information about past landscapes, this provides an indispensable resource for studying landscape evolution.

The collection

Central to a first phase of the development of this platform are the magnificent maps of the Arenberg family, which can be found in the General State Archives. The Arenberg family had possessions all over Europe, but mainly in the Low Countries. The collection has now been scanned at high quality and totals some 4,000 maps, ranging from parcel maps of villages to world maps.

An example of georeferencing in the Allmaps Editor

Contribute as a volunteer yourself!

Refine existing georeferences or get to work on toponyms. The results will be used for scientific research on past landscapes, making digital historical maps better available and as a test case to expand the platform with more functions and a multitude of historical maps. Go to the DigHimapper website and you can get started right away!

Contact: dighimaps@uantwerpen.be

DigHimapper is a collaboration between the University of Antwerp, the State Archives of Belgium, Webmapper and Bert Spaan. The project is funded by the Special Research Fund of the University of Antwerp and CLARIAH-VL. The original historical maps were transferred by the Arenberg Foundation to the State Archives of Belgium, which was responsible for scanning them. DigHimapper is also part of the FED-tWIN DigHimaps, a project collaboration between the University of Antwerp and the State Archives of Belgium, funded by BELSPO.