=Paper=
{{Paper
|id=Vol-2451/paper-27
|storemode=property
|title=Linked Stage Graph
|pdfUrl=https://ceur-ws.org/Vol-2451/paper-27.pdf
|volume=Vol-2451
|authors=Tabea Tietz,Jörg Waitelonis,Kanran Zhou,Paul Felgentreff,Nils Meyer,Andreas Weber,Harald Sack
|dblpUrl=https://dblp.org/rec/conf/i-semantics/TietzWZFMWS19
}}
==Linked Stage Graph==
https://ceur-ws.org/Vol-2451/paper-27.pdf
Linked Stage Graph
Tabea Tietz1,2 , Jörg Waitelonis3 , Kanran Zhou1a , Paul Felgentreff4 , Nils
Meyer5 , Andreas Weber5 , and Harald Sack1,2,3
1
Karlsruhe Institute of Technology, Institute AIFB, Germany
a
kanran.zhou@student.kit.edu
2
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany
firstname.lastname@fiz-karlsruhe.de
3
yovisto GmbH, Potsdam, Germany
joerg@yovisto.com
4
paul.felgentreff@gmail.com
5
Baden-Württemberg State Archives, Germany
firstname.lastname@la-bw.de
Abstract. Archives today are publishing their cultural heritage data
on the Web for exploration. However, for archive novices the traditional
archival structures often are not intuitive and difficult to understand, and
thus challenges data access and consumption. To tackle this problem,
Linked Stage Graph was developed, a knowledge graph (KG) on the
foundation of historical data about the Stuttgart State Theater. The
data was made available by the Baden-Württemberg State Archives for
the Coding da Vinci hackathon. This demo paper contributes the KG,
a SPARQL endpoint, named entity extraction and linking to existing
authoritative KGs as well as a dedicated user interface for exploration.
Keywords: cultural heritage · linked data · knowledge graph · UI.
1 Introduction
Digitizing cultural heritage has been a major task for galleries, libraries, archives
and museums (GLAM) for many years now. As a result, a number of Web based
platforms have been developed with the goal to enable researchers to access and
analyze the data scientifically as well as to allow the general public to explore the
data. However, many archival web platforms present their content organized in
a way familiar only to archive experts but users who are unfamiliar with archival
practise to structure information often find it challenging to access and explore
the provided content [1,2].
The Baden-Württemberg State Archives in Germany recognized this issue
and opened up their data to the Coding da Vinci initiative, the first German
open cultural heritage hackathon. The initiative organizes several hack events a
year, bringing together GLAM institutions as data providers and computer sci-
entists, designers, digital humanists to develop creative and interesting applica-
tions on the foundation of these cultural heritage data. The Baden-Württemberg
State Archives published a dataset about the Stuttgart State Theatres containing
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 Tietz et al.
XML2RDF
RDF
XML
(EAD-DDB)
VIRTUOSO
EAD2RDF RDF SPARQL ENDPOINT
EXPLORATION:
LINKED STAGE GRAPH VIEWER
slod.fiz-karlsruhe.de
COLORIZED
PHOTOS VIKUS VIEWER
Fig. 1: Workflow and architecture.
7.000 historical black and white photographs along with EAD-XML metadata
to be ’hacked’ by the creatives. The photographs and metadata cover the pe-
riod from the 1890s to the 1940s. Especially in Germany, this time period was
marked by social and political upheavals in which democracy, freedom of speech
and creativity in particular were challenged. For this reason, the data provided
are still of enormous relevance today. For instance, the data reveal which theater
performances were allowed to be played in these difficult times and how certain
characters were displayed [4]. In order to consume and optimally share these
data, specific requirements must be met. These include in particular interoper-
ability, availability and comprehensibility on different levels. Linked Data based
knowledge graphs (KG) have established as practical means to formally encode,
integrate and share data.
In this demo paper Linked Stage Graph 6 is presented, a KG developed during
the Coding da Vinci Süd 2019 hackathon7 on the foundation of the aforemen-
tioned archival data about the Stuttgart State Theaters. The goal of Linked Stage
Graph is to enable researchers as well as the general public to access, analyze and
explore the data in intuitive, interesting and useful ways. Along with the KG, the
presented prototype demo contributes a publicly available SPARQL endpoint8
to enable sophisticated queries for expert users, the extraction and linking of
named entities mentioned in the metadata to the Wikidata KG and the German
Integrated Authority File (GND)9 , a timeline interface for data exploration and
lessons learned. As an additional feature, all 7.000 black and white photographs
were colorized using open source tools based on machine learning (ML). All code
of this demo is published and freely available on GitHub10 .
2 Linked Stage Graph
This section presents the main contributions of the demo, Linked Stage Graph.
6
http://slod.fiz-karlsruhe.de/
7
https://codingdavinci.de/events/sued/
8
http://slod.fiz-karlsruhe.de/sparql
9
https://www.dnb.de/EN/Standardisierung/GND/gnd_node.html
10
https://github.com/ISE-FIZKarlsruhe/LinkedStageGraph
Linked Stage Graph 3
2.1 Dataset
The project is based on an archival fonds of around 7.000 historical photographs
from the Stuttgart State Theaters. The photographs depict scenes and characters
from a wide range of productions from opera to childrens theater dating from the
1890s to the 1940s. In 2009, the Ludwigsburg State Archives took the fonds into
their custody. It consisted of 572 sleeves containing prints, photographic plates,
nitrate films, photographic negatives and positive images. The archival descrip-
tion captures (where possible) the title and author of the play, the directors,
choreographers and designers of each production. The provided dataset consists
of JPEGs and an EAD-XML file (cf. Fig.1 left). The Encoded Archival Descrip-
tion11 (EAD) is a documentary XML standard for the description of archival
finding aids maintained by the Technical Subcommittee for Encoded Archival
Description of the Society of American Archivists together with the Library of
Congress. For users unfamiliar with an archives content structure it is difficult
interacting with EAD encoded finding aids, because often the archive’s hierarchy
has to be navigated to extract meaningful information [1,2]. Also, it has been
widely recognized that EAD complicates user interaction with the data because
operations like accessing a specific item on-the-fly are either impossible or in-
efficient. Automated processing is another issue since the degree of freedom for
expressing information within EAD is too high [6]. This attempts to overcome
these shortcomings by transforming the EAD-XML into an RDF representation.
2.2 Knowledge Graph
Fig. 1 presents the workflow. Before further processing the input XML file, some
adaptions were necessary like adapting the XML name spaces, adding reposi-
tory code and language code attributes to the EAD unitid XML-tags and re-
placing the lb tag with XML entity
. The actual XML to RDF conver-
sion was performed with two different approaches. First, the generic ReDeFer
XML2RDF12 converter was used. The second approach applied the EAD2RDF13
XSLT stylesheet to transform the provided XML to RDF. As expected, both
methods produced different results. While the EAD2RDF stylesheet result were
incomplete (e.g. did not manage to transform the information about the image
files), the XML2RDF converter produced a vast amount of blank nodes, which
are difficult to query and to navigate, but, the results were more complete. Both
methods created different IRIs to identify the actual subject of interest: the
archival unit. While XML2RDF preferred the archival identifier14 the EAD2RDF
transformation created the IRIs from the EAD unitid15 . From a computer science
11
https://www.loc.gov/ead/
12
http://rhizomik.net/html/redefer/
13
http://data.archiveshub.ac.uk/ead2rdf/
14
E.g. http://slod.fiz-karlsruhe.de/labw-2-2599382
15
E.g. from ’Abt. Staatsarchiv Ludwigsburg, E 18 III Nr 6’ http://slod.
fiz-karlsruhe.de/id/archivalresource/abt.staatsarchivludwigsburg,
e18iiibu161
4 Tietz et al.
perspective the first type of IRI was considered more stable, and that’s why it
was chosen as identifier. The results were merged through mapping the archival
unit titles and the archival identifiers. Finally, the unwanted IRIs were removed.
Many literals in the dataset contained unstructured information, i.e. the titles
included also a play’s author name and the abstracts contained further infor-
mation about involved persons and roles. To extract this information a script
was created. This also involved to define the vocabulary to model the persons
types of contribution and roles. The aim was to reuse existing vocabularies as
best as possible. Due to the clear definition of the domain the ambiguity in the
data was rather low. This enabled to map plays, person, and location names to
Wikidata and GND very quickly. Therefore, a dictionary of potentially relevant
resources form the vocabularies was extracted and an exact string matching was
performed. Finally, all information was deployed to an instance of the Virtuoso
triple store also providing a SPARQL endpoint.
2.3 Exploration
A variety of visual means was implemented and utilized to explore the archive
data. To bring the historical black and white images to life, they were automat-
ically colorized using an open source tool based on ML [7]. To oversee the RDF
data in a table view, an instance of the open source LodView RDF viewer was
adapted and deployed [5]. The Linked Stage Graph Viewer 16 was implemented
for this use case. The viewer is shown in Fig 2. It presents a timeline visual-
ization with the goal to let the user explore the rich and detailed images in a
more prominent way without too complex means of interaction to reduce the
technical barriers of engaging with the content. The user can scroll through the
images with an overview of the timeline on the right 1 . One large representative
image for each performance is shown 2 with further thumbnails on the bottom
3 . Swiping left or right reveals further plays which took place during the same
year 4 . Clicking on images will direct the user to the Lodview interface and
reveal all data available for the play. Next to the implemented Linked Stage
Graph Viewer, the Vikus Viewer 17 was utilized and connected to the dataset as
well. The viewer was previously developed by [3] and enables intuitive content
exploration in a timeline view as well as search and content clustering. During
the demo session, all described interfaces can be used to explore the dataset.
3 Conclusion
In this paper Linked Stage Graph is presented, a KG based on historical data
about the Stuttgart State Theater. Next to the KG, a SPARQL endpoint was
released, named entities mentioned in the metadata were extracted and linked to
existing KGs and a user interface was developed. The demo was created during
16
http://slod.fiz-karlsruhe.de/#Viewer
17
http://slod.fiz-karlsruhe.de/vikus/
Linked Stage Graph 5
1
2
4
3
Fig. 2: Linked Stage Graph Viewer
the Coding da Vinci Süd hackathon and was awarded the prize for the ”most
useful” application. Even though the presented demo implements a use case on
theater data only, it is generalizable to a broad range of archive domains since
the EAD-XML format is widely used in archives. Future work will involve the
improvement of the underlying ontology, a more exhaustive entity linking and
further means of visual exploration beyond timelines.
Acknowledgement. We would like to thank Coding da Vinci for connecting
cultural institutions with creatives to develop innovative applications.
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