SDHK meets NER: Linking place names with medieval charters
and historical maps
Olof Karsvall1 and Lars Borin2
1
The Swedish National Archives • TORA
2
Språkbanken, University of Gothenburg, Sweden • SWE-CLARIN
Abstract. Mass digitization of historical text sources opens new avenues for research in the
humanities and social sciences, but also presents a host of new methodological challenges. His-
torical text collections become more accessible, but new research tools must also be put in place
in order to fully exploit the new research possibilities emerging from having access to vast docu-
ment collections in digital format. This paper highlights some of the conditions to consider when
place names in an older source material, in this case medieval charters, are to be matched to ge-
ographical data. The Swedish National Archives make some 43,000 medieval letters available in
digital form through an online search facility. The volume of the material is such that manual
markup of names will not be feasible. In this paper, we present the material, discuss the promises
for research of linking, e.g., place names to other digital databases, and report on an experiment
where an off-the-shelf named-entity recognition system for modern Swedish is applied to this
material.
1 Introduction
Mass digitization of historical text sources opens fresh and exciting avenues for research in
the humanities and social sciences (HSS), but also presents a host of new methodological
challenges.
On the one hand, large-scale digitization of historical document collections certainly
serves to make these collections generally more accessible to researchers (and the interested
public), since they may now be browsed and searched at will on the internet, instead of by
going to a physical archive with restricted opening hours. Also, unlike the physical original
of a document, its digital version can be accessed by many individuals simultaneously.
On the other hand, in order to truly benefit from having large document collections in
digital format, and to enable new kinds of research, such collections must become more than
just digital versions of conventional physical archives.
One way of working with large-volume text collections for research purposes is often
referred to as text mining. Although not very strictly defined, this methodology generally
boils down to the application of data mining methods on large amounts of textual data. Data
mining in its turn presupposes that the data to be processed is formally structured along the
lines of conventional databases or spreadsheets. Thus, the key step in text mining is to turn
textual data into tabular data,3 i.e., in essence into metadata for a document or document part.
To the extent that these large digitized document collections have or can be equipped with
metadata, they can be processed with data mining methods, thus providing an alternative,
novel entry point for research. Further, making the data and metadata accessible on the web
inevitably leads to the possibliliy of data linkage across collections, which in turn raises the
issue of standardization of data and metadata, e.g., by spelling normalization and/or reference
to authority lists.
In concert with user interfaces for visualizing, browsing and manipulating data and re-
lations among formally structured data (Keim et al, 2010), text mining methods and linked
data provide powerful – and complementary – tools for discovering new facts and general-
izations from large volumes of data, since they allow us to build interfaces that combine a
“distant reading”/“macroanalysis” mode of inquiry (Moretti, 2005, 2013; Jockers, 2013) with
the “close reading” approach characteristic of traditional HSS research (cf. Schöch, 2013).
2 A concrete example: The Swedish medieval charters
There are several source materials from Sweden that can be said to be unusually rich and
useful, even compared with what we find in other countries. The medieval charters from the
1160s and onwards and the land survey maps from the 1640s and later on are two examples
of this. Most charters are legal documents, contracts between landowners concerning trans-
actions of land and other properties. The maps are also very detailed, specifying each farm
and all cultivated arable land.
These important source materials are used for addressing various historical research ques-
tions. The charters provide insights into a variety of transactions and events from the 12th
century to the 16th century. The oldest maps, dating back to the 17th century, provide details
of the agrarian landscape long before the major land reforms and urbanization.
Over the past 15 years, major efforts have been made to digitize these sources, and related
source editions, to make them accessible on the internet. This has enabled full-text search
which in some sense has revolutionized research on this material. Various events and objects
can now be searched and analyzed, something that previously required time-consuming man-
ual efforts. It is now easier than ever to formulate and explore different research questions.
Digitization also opens up for quantitative analysis, as a result of simultaneous availability of
larger collections of data.
However, a limitation currently is that the databases and search applications are modeled
and developed as separate systems. Each source material has its own character and metadata.
But each source material also contains general information that appears in other databases
and registers. Names of persons and places are a typical type of information that often occurs
3
In this context, text is often characterized by computer scientists as “unstructured data”, which of course to a
linguist sounds very much like turning things on their head.
in several sources. Names are therefore suitable as authority files on the semantic web, which
many can use and refer to as linked data. A risk today is that research teams and developers
will create their own name definitions for a lot of objects that already exist as data on the
web, with duplication as a consequence.
The major point of using linked data as an approach is that it connects historical data
and enables research that ‘combines’ data in a new and easy way. One example of a research
question that could be highlighted concerns land ownership in the Middle Ages, where an
identification of the people who buy and sell land – which is documented in the medieval
letters – can be combined with the coordinates of the settlements of land ownership, which
can be extracted from the historical maps.
Digitization has come a long way but we could go further. Often the databases have been
designed mainly for internal use, as authoring tools for curators of the data. A great deal of
effort remains in defining, labeling and linking the content of the sources. All these historical
‘knowledge bases’ taken together carry a significant potential for further research that has
been little used. At present, they are not adequately combined and adapted to each other.
This paper focuses on geographic annotation methods, and more precisely the question
of how place names in medieval charters can be identified and matched to geographical data
from historical maps. The number of place names in all these medieval letters is not known,
but very likely they hold more than one hundred thousand place names. Manual markup of
these place names is thus not feasible. Instead, a method that automatically identifies place
names is needed.4 In section 4 below, we describe such a method, but first we now turn to a
more detailed technical description of our data.
2.1 Sources and data format
Since 2003, the digital edition of the medieval charters in Sweden (the Diplomatarium Main
Catalogue, SDHK) has been made accessible over the internet (see figure 1). SDHK is a
database with transcriptions and/or summaries of text from over 43,000 medieval charters
in Sweden, from the 12th century until the early 16th century.5 The SDHK is a first portal
for anyone looking for information on Swedish medieval charters. The web interface to the
4
Names of persons in the medieval charters could very likely be identified in a similar way as place names.
However, personal names will not be investigated in this paper simply because it requires more. Many
people named are not known, and the identification often requires access to registers which have not yet
been digitized, for instance Sveriges medeltida personnamn: http://www.sprakochfolkminnen.
se/om-oss/om-webbplatsen/andra-sprak-an-svenska/english.html.
5
The traditional editorial work of Diplomatarium Suecanum – the chronological national edition of Swedish
medieval charters – started already in the 1820s. Having now completed the 1370s, the Diplomatarium series
continues and new books are published on a regular basis.
Fig. 1. The Swedish National Archives’ online search interface to SDHK
database permits the users to search for persons, places and various subjects in the document
summaries and in the full texts.6
It is important to emphasize that the preserved charters and documents deal above all
with various property transactions, and as a consequence a large number of place names and
settlements are mentioned.
The database is based on XML documents using the TEI (Text Encoding Iniative) markup
language.7 Each XML file – corresponding to one medieval charter – contains metadata,
a summary of the contents and the full literal transcription in the original source, and if
applicable, references and comments. Besides the transcription of the text, some metadata
has been registered concerning the original document, e.g. year of issue, issued by whom,
place of issue, etc.
In addition to , the element is used, which consists of ,
and . The full transcription of the original letter is reproduced within . The
full text, which is either in Old Swedish or Latin, is not normalized. Each place name can
be spelled in many different ways. Hence, it is difficult to automatically identify the place
names in the content of the tag.8
The focus of this paper will therefore be on the element and the editorial sum-
mary of each charter, where most known place names are listed in Swedish modern spelling.
6
The database is a part of the Swedish National Archives and is available at the National
Archives website http://sok.riksarkivet.se/sdhk, see also https://riksarkivet.se/
facts-medieval-charters.
7
http://www.tei-c.org
8
Tags are defined in the TEI: P5 Guidelines http://www.tei-c.org/Guidelines/P5.
Fig. 2. SDHK 8953
Within the element, place names occur within the tag, where
specify when the charter was issued, and specify the location of issue. Since
all issuing locations are already marked as , it is thus possible to match those
names with authority files and geodata, for example GeoNames or – what is preferable in this
case – the historical geodata registered in TORA (see below).
There are also other types of locational data related to the Swedish medieval charters.
Most place names appear within a tag that holds the summary of the transcription (the
text under the heading “Contents” in figure 2). The summary contains one or more paragraphs
, without any annotations of the place names, persons etc. In order to geocode the place
names mentioned in the summary – which are essentially all the names that are also present
in the entire text of the text – these must first be identified as place names, i.e.
according to the TEI format. After that they could be matched with geodata.9 How this could
be done is discussed in the following sections.
3 Geocoding historical places
A place name points to a geographical location. Regarding medieval charters most place
names refer to settlement units such as towns, manors, villages, hamlets and single farms,
9
Writing summaries of the medieval charters is an ongoing editorial activity at the Swedish National Archives.
The letters from the years 1360–1380 have the most detailed summaries. Later on, more place names will be
added to the summaries of the documents from other periods.
henceforth referred as historical settlement units.10 There are several tools on the internet
that could be used to mark a location on a modern map, as a point or area, and retrieve this
data in any format, such as TEI or GeoJSON. It would thus be possible to add coordinates
directly to the digital edition of a particular source material.
However, a better approach would be to match place names with existing geodata, pro-
vided by for instance GeoNames. Initiatives have also lately been taken to create open geo-
data for historical sites. One such initiative is the Pelagios project, that connects several sets
of geodata, for instance the Digital Atlas of the Roman Empire.11
Analyses of historical documents in relation to spatial data should have a great potential
in several research areas. The administrative divisions of Denmark have been digitized within
the DigDag project.12 Mapping and GIS techniques are also regularly used and adapted by
researchers, for example, to analyze the routes taken by individuals (Storm et al, 2017). Spa-
tial data from historical maps has enabled identification of old cultivated hops (Strese et al,
2009). Data extracted from historical maps is the basis for an identification of late-medieval
deserted farms (Karsvall, 2016).
The project TORA – Topographical Register at the National Archives in Stockholm –
specifically defines the historical places in Sweden that existed from the Middle Ages un-
til about the 18th century.13 The aim is to link digitized historical sources to well defined
geodata. TORA includes place names and coordinates – in the form of points – for all set-
tlement units that appears in the oldest land survey maps from the 17th century (Höglund,
2008; Tollin and Karsvall, 2010). Moreover, a large amount of settlements that appear in
the Crown’s cadastres (jordeböcker) in the mid-16th century are also included.14 At present
TORA holds about 22,000 spatial coordinates, which are published as linked data in RDF
format.15,16
The coordinates in TORA are set at the actual location of the settlement, according to the
oldest map available for each site. The accuracy is specified as high, medium or low. A large
10
A historical settlement unit refers to a place mentioned in an historical source, here the medieval charters.
11
http://commons.pelagios.org;http://dare.ht.lu.se
12
http://www.digdag.dk/
13
http://riksarkivet.se/tora
14
Starting in the 1960s, the project Medieval Sweden (Det medeltida Sverige) extracts information from the
medieval documents, as well as the economic provincial material of the 16th century (landskapshandlingarna).
TORA includes coordinates of all settlements published in the book series of Medieval Sweden. So far 23
book volumes has been published, covering most parts of south-east Sweden: https://riksarkivet.
se/medieval-sweden-dms
15
Resource Description Framework: https://www.w3.org/RDF/
16
The historical settlement units are published in RDF format (a W3C standard) using serial numbers, e.g.,
data.riksarkivet.se/tora/1. TORA uses the EntryStore platform developed by MetaSolutions AB
(http://entrystore.org/). For further information see http://riksarkivet.se/tora.
number of abandoned settlements, not existing today, also appear in TORA, which is thus the
most relevant authority file that can be linked to the place names in the medieval charters.17
3.1 An example
This relatively typical case refers to a medieval charter from 1366, issued in Eskilstuna in
south-eastern Sweden (SDHK 8953; see figure 2). The original letter, written in Latin, has
been fully transcribed and interpreted.18 The place of issue, Eskilstuna, that already has a
tag, can be assumed to correspond to the town of Eskilstuna. However, the
town is of much later date (17th century). Most likely this charter has been issued at the
monastery in Eskilstuna, which was established in the 12th century. The monastery was aban-
doned in the 16th century and replaced by a castle on the same spot. The corresponding set-
tlement units and coordinates for Eskilstuna mentioned in this charter would therefore much
likely be ‘Eskilstuna Slott’, as defined in TORA using the oldest maps from year 1647.19
Eskilstuna is mentioned once more in the tag in the digital edition in SDHK,
as ‘Eskilstuna kloster’, which refers to the monastery. What would complicate an automatic
match is the fact that ‘Eskilstuna kloster’ in more recent periods has become the name of
the parish in this area. In GeoNames, for example, the coordinates linked to the name of
‘Eskilstuna kloster’ points to the parish area and not the location of the monastery.20
The same charter, SDHK 8953, holds one more place name, Torshälla, just north of Es-
kilstuna. TORA has two registrations of Torshälla. One that refers to the town, which dates
from the early 1300s, and one that refers to the neighboring settlement of a vicarage. It is not
clear which of the two that is intended. In such cases it could be useful to make a temporary
match that includes the other options available as alternatives.21
4 Towards automatic name tagging in SDHK
As mentioned above, every document in the SDHK database has a human-composed sum-
mary written in modern Swedish (labeled “Contents” in figure 2), although the language of
the summaries sometimes has an archaic feel to it, especially when the original letter is in
Old Swedish (and not in Latin as the example in figure 2).
These summaries form the point of departure for the experiment described in this pa-
per. Employing state-of-the-art natural language processing and information extraction tech-
niques, the summaries can be used as a rich source of metadata for the underlying documents,
17
The method of how to set coordinates for settlement units using historical maps is the subject of a forthcoming
article by the first author.
18
https://sok.riksarkivet.se/bildvisning/Sdhk_8953.jpg
19
https://data.riksarkivet.se/tora/2675; https://data.riksarkivet.se/tora/
2675; https://sok.riksarkivet.se/bildvisning/R0000151_00049
20
http://www.geonames.org/8132118/eskilstuna-kloster.html
21
https://data.riksarkivet.se/tora/12463; https://data.riksarkivet.se/tora/
2733
complementing the manually compiled metadata already accompanying them. At least the
kinds of (legal) action, the involved individuals and other agents, and the involved locations,
should be retrievable from the summaries with automatic methods.
Given the aim expressed above of linking SDHK to the historical place name register
TORA, in our first experiment described here, we apply a named-entity recognition (NER)
system designed for modern Swedish to the summaries and evaluate its accuracy on data
which – although written in the modern language – may contain many names which are no
longer current.
4.1 Experimental setup
For this experiment, 14 documents were sampled from the SDHK database and their sum-
maries extracted, amounting to a total of approximately 1,500 words.22 All names of persons
and places were manually identified and annotated in the text of the summaries, in order to
have a gold standard dataset for evaluating the automated NER accuracy.23
For the NER we use a rule-based system developed over many years in Språkbanken at
the University of Gothenburg (Kokkinakis et al, 2014). It has already proven its worth in
some digital humanities applications, although with textual data of more recent date than in
the present case (Borin and Kokkinakis, 2010; Borin et al, 2014).
NER is one of the annotations available through Språkbanken’s Sparv infrastructure
(Borin et al, 2016; see figure 3). Sparv exposes the linguistic annotation pipeline used as
the first step in Språkbanken’s Korp corpus infrastructure (Borin et al, 2012).
Sparv also makes available a corpus upload function for offline processing of larger vol-
umes of text. In this way the 14 SDHK summaries were linguistically annotated.
4.2 Experimental results and discussion
Table 1 shows the accuracy figures for the two types of names.24 We note that the recall
for personal names is much better than that for place names, i.e., most personal names in the
22
The sample comprised documents SDHK 2382, 8846, 8847, 8861, 8887, 8896, 8901, 8913, 8931, 8953, 8954,
8955, 8959, 8970.
23
Using gold standard data for evaluating an automatic process is considered absolutely de rigueur in natural
language processing, as the only way of obtaining an objective and reproducible measure of the accuracy
of the result of the process. In the case at hand, this means that the automatic NER is applied to the same
documents that were manually annotated but with the manual annotations hidden from the automatic process,
and accuracy is then computed from a comparison of the NER annotations with the manual annotations.
24
The figures have been calculated so that partial matches and full matches are counted as equal, i.e., a NER
place name match for Nöttja is considered a full match for the manually identified Nöttja socken ‘Nöttja
Parish’. The measures in the last two columns of the table are the standard information retrieval evaluation
measures of recall and precision. Recall is defined as the share of all true instances in the dataset that were
actually retrieved, and precision as the share of all the retrieved instances that are true instances. Thus if a
dataset contains 50 true instances and a retrieval method returns 40 hits, out of which 35 are true instances,
the recall in this case will be 0.70 (35/50), whereas the precision will be 0.88 (35/40).
Fig. 3. Språkbanken’s Sparv corpus annotation interface
texts are in fact recovered by the system. Since the NER system relies primarily on gazetteers
(name lists) for the recognition of both, a possible reason for this could be that many of the
personal names used in the Middle Ages are still around, while place names have seen a
higher replacement rate over the intervening centuries.
Table 1. NER accuracy figures
SDHK NER
name type total correct false missed precision recall
Place name 114 62 4 48 0.94 0.54
Personal name 100 92 8 8 0.92 0.92
The precision of the NER system used in this experiment is comfortably high for both
name types, i.e., there are few false positives in both place and personal name recognition.
This means that even out of the box, this NER system could provide considerable added value
to the digital resources of the National Archives as research data sources.
Finally, we note that the Sparv annotation pipeline is made up from independent tools of
different origin. This means that the tools do not normally draw on each other’s annotations,
something which is clearly reflected in the results of this experiment, if we take all annota-
tions into account. Consequently, the named entity recognizer does not utilize the information
from the part-of-speech tagger, which in fact tags 42 additional place names and 4 additional
personal names as proper nouns (tag PM), but also gives 15 false positives, i.e., non-names
tagged as proper nouns. This gives a recall rate (for names) of 0.82 and a precision of 0.75 and
recall rates for place names and personal names of 0.88 and 0.50, respectively. Distributing
the false positives proportionally between the two classes of names, we end up with estimated
precision figures for place names and personal names of 0.75 and 0.80, respectively.
The analysis, using NER, has so far been targeting the occurrences of the names. How-
ever, place names could refer to several geographical locations. Common names of settle-
ments, which are found in our survey material, are e.g. ‘Berga’, ‘Lundby’ and ‘Söderby’. A
further geographical identification of the sampled data – the 14 medieval charters with al-
together 144 place names – is therefore needed. For this purpose TORA will be used. The
geographic accuracy in the NER system and the National Land Survey database (Lantmä-
teriet, LM) will also be assessed.
The result, presented in Table 2, is based on a semi-automatic method which has two
steps: firstly, the place names in the XML documents of the medieval letters have been
mapped with the name lists discussed here (TORA, NER and LM); secondly, a manual check
has been conducted to evaluate if the matched names refer to the geographical locations
mentioned in the documents. It should be emphasized that medieval charters are difficult to
interpret and that spatial localization sometimes requires expert analysis. In our sample data,
however, all names (with one exception) are normalized, written in modern Swedish.
Table 2. Geocoding of place names in medieval charters
source correct uncertain missed precision recall
NER 41 25 48 0.62 0.36
TORA 46 6 62 0.88 0.40
LM 41 24 49 0.63 0.36
As shown in Table 2, TORA, NER and LM provide an equivalent result with a recall
rate between 0.36 and 0.40. All three recognize the names of major towns. Also unique and
unusual place names are matched with their geographical location. Foreign place names (25
in our sample data) could not be recognized by TORA or LM, as they are limited to the
current borders of Sweden.
Overall, TORA is the better recognizer. Especially since TORA has the most accurate
coordinates of historical places as discussed before (see section 3). It does not cover general
names of countries (as ‘Sweden’) and provinces (as ‘Uppland’). On the other hand, it includes
all parishes and also other historical administrative divisions such as hundreds (härader).
The strength of TORA is above all that it recognizes most settlement units, i.e. villages
and hamlets. Only six names in this experiment are uncertain giving a precision of 0.88. In
comparison, NER and LM show a geographical precision of 0.62 and 0.63. As shown in Ta-
ble 1, NER successfully finds 62 place names. Of these 41 names can be assumed to point
out the correct location. The uncertainty is mainly due to the occurrence of common place
names as ‘Stenby’, ‘Lundby’, ‘Valla’ among others. With TORA – which includes adminis-
trative divisions, such as parishes —-it is easier to determine the geographical location of a
place name, even if it occurs frequently. The total amount of place names and coordinates in
TORA is at present limited. About 50 percent of all historical settlement units are included
so far. Names not yet included can, however, be added. As long as TORA is not complete
NER and LM will be necessary complements. LM holds all official place names in Sweden
today, but it does not cover historical settlements, e.g. disbanded place names and abandoned
settlements.
In conclusion, the results of this small experiment are promising, and there is also clearly
room for improvement of the NER system. In order to make this technology useful in the
present context, the next natural step would be to match recognized names to authority lists
such as TORA that give the names an identity, and makes them suitable as linked data. This
would serve to add meaningful identities to place names in the medieval documents, by pro-
viding accurate coordinates that point to settlement locations specified in the oldest large-
scale maps.
5 Conclusions
Since the name information in the Swedish charter today lacks clear identifiers and spatial
coordinates, it is difficult to make statistical calculations and any kind of spatial comparative
studies. One solution to these problems would be to relate historical data of this kind to actual
physical location by using coordinates. To a large extent, matching can be done automatically
based on name.
We investigated the feasibility of applying a named entity recognition system on the
text of the SDHK document summaries, with quite promising results and some indications of
how the NER system could be improved, and we intend to pursue this work further, including
how to utilize the added metadata in user interfaces for researchers. This kind of ‘coordinate-
based’ linked databases would present quite new conditions and possibilities within a num-
ber of disciplines, e.g. history, historical geography, social history, philology, onomastics and
archaeology. Irrespective of administrative divisions, data can be collected freely, and pre-
viously unknown connections may appear. Large sets of data can be extracted intended for
national surveys illustrating for instance regional differences. The scholars and users can de-
vote their time to formulating research questions and making analyses instead of collecting
and preparing the research material.
Acknowledgements
The research presented here is part of a project (TORA) funded by the Royal Swedish
Academy of Letters, History and Antiquities, Riksbankens Jubileumsfond, and the Swedish
National Archives. The work has also drawn on the e-infrastructure Swe-Clarin, funded by
the Swedish Research Council (contract 2013-2003) and by the collaborating partner institu-
tions forming Swe-Clarin (University of Gothenburg, The Institute of Language and Folklore,
KTH, Linköping University, Lund University, The National Archives, Stockholm University
and Uppsala University).
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