=Paper=
{{Paper
|id=Vol-2941/paper10
|storemode=property
|title=Cinema Context as Linked Open Data: Converting an online Dutch film culture dataset to RDF
|pdfUrl=https://ceur-ws.org/Vol-2941/paper10.pdf
|volume=Vol-2941
|authors=Leon van Wissen,Thunnis van Oort,Julia Noordegraaf,Ivan Kisjes
|dblpUrl=https://dblp.org/rec/conf/i-semantics/WissenONK21
}}
==Cinema Context as Linked Open Data: Converting an online Dutch film culture dataset to RDF==
https://ceur-ws.org/Vol-2941/paper10.pdf
Cinema Context as Linked Open Data?
Converting an online Dutch film culture dataset to RDF
Leon van Wissen1[0000−0001−8672−025X] , Thunnis van Oort2[0000−0001−8912−0508] ,
Julia Noordegraaf1[0000−0003−0146−642X] , and Ivan Kisjes1
1
University of Amsterdam, The Netherlands
{l.vanwissen, j.j.noordegraaf, i.kisjes}@uva.nl
2
Radboud University Nijmegen, The Netherlands
thunnis.vanoort@ru.nl
Abstract. This paper describes the process of converting Cinema Con-
text, an online dataset on Dutch film culture, into Linked Open Data.
It covers our experiences in this conversion process from the steps of
data cleaning and modeling, up to publishing and evaluating the result
through a case study.
Keywords: Cinema History · Digital Humanities · Linked Open Data
1 Introduction
Cinema Context (CC) is an online encyclopedia on Dutch film culture since 1896
[1]. Built on top of a MySQL database the website www.cinemacontext.nl offers
both an informational view as well as a research environment on places, persons
and companies involved in more than 100k film screenings in the Netherlands.
The website allows a visitor to search and extract the data, though this is limited
to the offered capabilities of the faceted search. To provide access to the full
dataset and to boost its interactivity, we have now published it as RDF.
In this paper, we describe the process of converting a relational database
with Cultural Heritage (CH) data into Linked Open Data (LOD). It also gives
an example of the potential that this format offers. Specifically, converting this
dataset to LOD brings opportunities for broadening and renewing historical and
cultural research by allowing more flexible linking to other (linked) datasets on
for instance buildings, persons, heritage objects, and locations. Researchers in
the Digital Humanities (DH) and CH communities have indicated a need [5] to be
able to query CC in connection with external data via e.g. a SPARQL endpoint,
in order to research the role of cultural and socio-economic status in processes
of cultural consumption. Moreover, the selection of appropriate vocabularies
and thesauri required close collaboration between data specialists and domain
experts and has functioned as de facto training in working with RDF and the
SPARQL query language for scholars working in DH.
?
This project was partly financed by a DANS Small Data Project that stimulates
projects that adhere to the FAIR guiding principles for scientific data management.
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 Van Wissen et al.
2 Model
The CC data model [6] has its foundation in five interconnected entity types:
persons, companies, venues, films, and screenings. These core entities are given a
persistent identifier which serves as URI. Due to its ease of use and its increasing
applicability in the DH and CH the schema.org vocabulary is particularly
suitable for modeling the contents of CC. Although the database is not aimed
at describing present-day or future events, the individual entities in CC contain
sufficient information to model the classes and properties in this vocabulary.
How we model these entities is described below (see Fig. 1).
2.1 Entity types
Place Organization parentOrganization
location parentOrganization worksFor
MovieTheater organizer author Person
location
containedInPlace
Event Rating
WikiData publisher
subEvent
sameAs
IMDB id
ScreeningEvent contentRating
about
sameAs CC id
workPresented Dutch National
IMDB
Archives
sameAs
Country countryOfOrigin Movie isPartOf
Fig. 1. Classes and their interrelation in the CC RDF data. The white nodes are classes
from the schema.org vocabulary.
Films and ratings We model each film that is described in CC as a schema:Movie
and provide information on its name, alternate name, production year, country of
origin, format, and extent. A schema:sameAs property is available for every film
to refer to its entry on www.imdb.com. Ratings for these films are modeled as
schema:Rating and schema:CreativeWork and come from the archive of Film
Screening Reports 1928-1960 which is held by the Dutch National Archives. The
value in the schema:sameAs property of a rating points to the respective index
in this collection.
Persons Persons in the CC data can be owners or employees of theaters and
companies. They are modeled as schema:Person, provided with biographical
information such as birth dates if available. A person’s name is modeled through
the Person Name Vocabulary (pnv)3 .
3
https://w3id.org/pnv#
Cinema Context as Linked Open Data 3
Venues and companies Theaters or venues are organizations situated at a
specific location (schema:Place, including geometry) and with a specific name.
Usually, they are owned by a company or person. Each theater is a schema:Movie
Theater, or a schema:EventVenue for general-purpose venues. If available, in-
formation on the seating capacity, number of screens, and lifespan is also given.
The companies in this dataset are organizations that run cinemas or distribution
companies and are modeled as an instance of schema:Organization.
Events and screenings We distinguish two event classes: schema:Event for
cinema programs, and sem:Event from the Simple Event Model (sem)4 for
generic events, such as a venue’s construction history. Instances of the first con-
sist of one or more schema:subEvents of type schema:ScreeningEvent (filmic)
or schema:TheaterEvent (non-filmic), held in a specific theater on one or more
dates, using a particular program name. A schema:startDate indicates the day
a program started: normally, a cinema program would be screened for a week.
Deviations from this norm are usually annotated as schema:description.
2.2 Other and qualifiers
The schema.org vocabulary is complemented where it falls short, for instance
when describing person names, legal entity types, and special cinema types (e.g.
traveling cinema), but also when expressing a film’s length and extent, for which
we use properties from Dublin Core5 . Additionally, if a date is given in a less
precise format than xsd:date, then the sem time stamp properties have been
used to supply a proper date value. For consistency and usability, and to indicate
a temporal restriction or (un)certainty, the sem properties are always present,
even if an exact date is given.
This is also the case when resources are temporally restricted. We use the
schema:Role class to express a specific time frame in which a certain property
value relation is valid. This class can be used in any object position and extends
the triple with the same property, whilst incorporating additional information
as qualifier, such as specific roles some entity played, or a start and end date.
This way of modeling is used consistently in the data to boost its queryability,
even when there is nothing to qualify. A description of less prominent auxiliary
classes can be found in the dataset’s documentation pages (see Section 3).
3 Documentation and code
Documentation pages [3] were built to accompany the constructed RDF and in-
clude an explanation of the used vocabulary, modeling and SPARQL query ex-
amples, reports on hands-on sessions, and general information about the project.
4
http://semanticweb.cs.vu.nl/2009/11/sem/
5
http://purl.org/dc/terms/
4 Van Wissen et al.
Both the documentation and the code that converts the MySQL database
are available in a git repository [3]. A pipeline is built in such a way that new
LOD can be generated instantly whenever a new dump of the database is made.
The latest dump of the MySQL dataset can be found at DANS [2].
4 Case Study: International Orientation Index
A case study6 serves to illustrate the potential of connecting the CC dataset
with other knowledge graphs. It replicates the analysis of economic film histo-
rian Peter Miskell et al. [4] and their ‘international orientation index’. Miskell
et al. propose this index to investigate the relative success of Hollywood pro-
ductions abroad in the post-war reconstruction period and state that American
productions with a high proportion of non-American creative talent and content7
have fared better at non-American box offices.
We can test this hypothesis for the Dutch film market by analyzing program-
ming data from CC. What is missing in our dataset is information on box office
revenues, but this value can be approximated by the number of screening weeks
under the assumption that a film with more screenings generates higher revenue.
To approximate the variables Miskell et al. used to construct their index, we can
apply the information available for films in Wikidata. Instead of assigning a 0,
1 or 2 score to a criterion, we assigned a relative score (0.0-1.0) to the variables,
indicating the extent of ‘internationalisation’ (or rather: ‘non-Americanness’) in
a category. For a total of 8,836 films (5,495 Hollywood productions), we gathered
information on six categories through a SPARQL query, each retrieved with a
particular Wikidata property path (e.g. the film’s director, followed by his/her
country of citizenship). Examples of this calculation are shown in Table 1.
Calculating a correlation coefficient between the number of screenings and
the relative internationalness of the Hollywood produced part of our corpus by
using Pearson’s r indicates that there is a very weak correlation of 0.130 when
we consider films (N=3418) for which we have information in at least three
categories and 0.137 when we consider films (N=1340) for which we have at least
four categories (below three is not sufficiently representing internationalness;
over five reduces the corpus size too much). Though positive, and thus indicating
that internationally-oriented films in the Netherlands perform slightly better
than fully American ones, we should further refine this proof of concept in future
studies before making solid claims.
5 Summary
This project shows that the schema.org vocabulary can easily be applied to
cultural heritage data and deemed fit for modeling our (research) dataset. With
6
A more detailed explanation of this and other case studies, including code and data,
can be found in the documentation pages [3] under ‘events’.
7
Measured based on (1) nationality of leading actors, directors, screenwriters, (2)
setting, and (3) national provenance of the source text.
Cinema Context as Linked Open Data 5
Table 1. Individual examples of calculating this score. The relative total scores are
calculated by dividing the total score over the number of available variables.
Category Anna Karenina (1935) Casablanca (1942) Key Largo (1948)
CC id F001809 F020802 F015663
Wikidata id Q561208 Q132689 Q830773
Screenings 37 28 No data available
Director 0.0 0.50 0.0
Screenwriter 0.5 0.0 0.0
Cast 0.53 0.58 0.125
Narrative 1.0 1.0 0.0
Shooting No data available 0.0 No data available
Source author 1.0 0.0 0.0
Total (relative) 3.03 (0.61) 2.08 (0.35) 0.125 (0.03)
Miskell et al. 12 7 1
some small additions, we were able to capture and publish this dataset in LOD,
and thereby make it more readily available for (re)usage in the DH and Social
Sciences. The case study demonstrates how such a dataset can be operationalized
in the workflow of a DH research project. For the time being, the LOD version of
CC exists besides the original database and accompanying website, but ideally,
these will be merged and/or further integrated in a future version.
Acknowledgements
The project was a collaboration between the CC editorial staff, Library UvA,
and Menno den Engelse (Islands of Meaning).
References
1. Dibbets, K.: Cinema Context and the genes of film history. New Review of Film
and Television Studies 8(3), 331–342 (2010)
2. Dibbets, K.: Cinema Context. film in Nederland vanaf 1896: Een encylopedie van
de filmcultuur (2018). https://doi.org/10.17026/dans-z9y-c5g6
3. den Engelse, M., van Wissen, L., van Oort, T., Noordegraaf, J.: Cinema Context in
RDF (2020). https://doi.org/10.17026/dans-z64-mrvb, https://uvacreate.gitlab.io/
cinema-context/cinema-context-rdf/
4. Miskell, P., Li, Y.: Hollywood studios, independent producers and international
markets: Globalisation and the US film industry c. 1950–1965. Henley Business
School (2014)
5. Noordegraaf, J., et al.: Semantic deep mapping in the Amsterdam Time Machine:
Viewing late 19th- and early 20th-century theatre and cinema culture through the
lens of language use and socio-economic status. CCIS (2021 forthcoming)
6. van Oort, T., Noordegraaf, J.: The Cinema Context database on film exhibition and
distribution in the Netherlands: A critical guide: Arts and media. RDJ for the SSH
5(2), 91–108 (2020). https://doi.org/10.1163/24523666-00502008