=Paper=
{{Paper
|id=Vol-3865/03_paper
|storemode=property
|title=Constructing a Knowledge Graph for Italian Cinema Divas’ Autobiographies (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3865/03_paper.pdf
|volume=Vol-3865
|authors=Giorgio Corona,Dario Guidotti,Laura Pandolfo
|dblpUrl=https://dblp.org/rec/conf/aiia/CoronaGP24
}}
==Constructing a Knowledge Graph for Italian Cinema Divas’ Autobiographies (short paper)==
https://ceur-ws.org/Vol-3865/03_paper.pdf
.
Constructing a Knowledge Graph for Italian Cinema Divas’
Autobiographies
Giorgio Corona, Dario Guidotti and Laura Pandolfo*
DUMAS, Università degli Studi di Sassari, via Roma 151, Sassari, 07100, Italy
Abstract
Autobiographical writings are invaluable for research, offering relevant insights into personal experiences and
cultural contexts. This is particularly true for Italian actresses, whose autobiographies, while rich with information,
have been relatively underexplored in academic research. The Women Writing around the Camera (WOW)
project addresses this gap by developing a semantic portal dedicated to these autobiographical texts. The WOW
portal will reveal the dynamics between the actresses’ writings, their private lives, their artistic careers, and the
cultivation of the diva image. As a first step towards this goal, this paper presents the WOW knowledge graph
(KG), which maps the personal and professional networks related to the divas’ lives. The KG was built starting
from the actresses’ autobiographies, guided by a taxonomy of themes curated by domain experts. Although still
under development and expansion, the KG provides a solid foundation for future enhancements.
Keywords
Linked Open Data, Semantic Web, Cultural Heritage, Digital Humanities, Film and Literary Studies
1. Introduction
Autobiographies are not merely collections of biographical data. They are intricate narratives that
integrate personal experiences, emotions, and reflections, offering a depth that goes beyond mere
factual recounting. As such, they serve as invaluable resources for researchers seeking to explore and
understand historical contexts and personal histories [1]. Traditionally, autobiographies are analysed
through close reading [2], a manual method that offers detailed insights into personal narratives without
the aid of computational tools or automated analysis. However, there is a growing interest in publishing
autobiographical information as Linked Open Data [3] and leveraging Knowledge Graphs (KGs) [4].
This approach not only facilitates computational analyses typical of distant reading but also leverages
Semantic Web tools and technologies, enabling a wider range of analytical and semantic methods [5, 6, 7].
By structuring autobiographical data as Linked Data, researchers can uncover patterns, relationships,
and insights that might be missed through close reading alone, thus enriching the understanding of
the text and expanding data-driven exploration opportunities. However, converting autobiographical
information into Linked Data presents significant challenges due to the unique and complex nature of
such data, necessitating careful handling and representation.
In this paper, we present our efforts in developing a knowledge graph within the context of the
Women Writing around the Camera (WOW) project [8], which aims to create a semantic portal in the
field of humanities that collects and organises data related to the autobiographies of Italian actresses,
known as divagrafie [9]. The presented knowledge graph serves as the foundational knowledge base
of the portal, mapping the personal and professional networks associated with the lives of Italian
cinema divas, e.g., Sophia Loren, Monica Vitti, and Franca Valeri. By focusing on the dynamics between
actresses’ writings and films in which they performed, our work aims to integrate isolated datasets into
a cohesive and harmonised KG. The data we use includes biographical and bibliographic information,
3rd Workshop on Artificial Intelligence for Cultural Heritage (AI4CH 2024, https:// ai4ch.di.unito.it/ ), co-located with the 23nd
International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024). 26-28 November 2024, Bolzano, Italy
*
Corresponding author.
$ gcorona1@uniss.it (G. Corona); dguidotti@uniss.it (D. Guidotti); lpandolfo@uniss.it (L. Pandolfo)
0009-0008-6039-4933 (G. Corona); 0000-0001-8284-5266 (D. Guidotti); 0000-0002-5785-5638 (L. Pandolfo)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
along with a comprehensive taxonomy of concepts and keywords that encapsulate the key themes for
studying the lives of Italian actresses. To construct the KG, we began with semi-automatically generated
annotations made by domain experts on the autobiographical texts, in a manner similar to that applied
in [10, 11]. These annotations were then transformed into triples in Resource Description Framework
(RDF) format, integrating with existing data sources from the Linked Data Cloud. This amount of data
allows scholars to explore the lives and careers of these actresses from a broader perspective.
This paper is organised as follows. Section 2 presents relevant examples of datasets and structured
knowledge in the cultural heritage domain concerning literary texts and biographical materials. Section
3 presents the methodology used for implementing the KG, by describing source datasets, data model
and the population of the dataset. An analysis of the data quality and usefulness is given in Section 4,
while conclusion is provided in Section 5.
2. Related Work
The digitisation and semantic enrichment of corpora have significantly transformed the accessibility
and analysis of biographical and literary data. Several initiatives have developed datasets to facilitate
cataloguing and study of such data in a variety of research contexts. Some examples include the
following:
• BiographyNet [12] is a project that enables semantic analysis of biographical dictionaries from
the 18th century to today. Its main goals are to facilitate prosopographical analysis of social
groups, examine biography topics, and provide insights into historiographical approaches across
countries and languages.
• BiographySampo [13] is a semantic portal based on Finland’s National Biography Collection,
transforming biographical texts into Linked Data. It offers advanced tools for data visualization,
analysis, and knowledge discovery, specifically designed for prosopographical research in the
humanities.
• The World Literature Knowledge Graph [14] features over 194,000 writers and 971,000 works,
enabling exploration of global literature and authors. It integrates data on the reception of these
works across reader communities within a unified semantic model.
• WarSampo [15] represents a significant application of knowledge graphs in historical research.
By integrating diverse sources like Finnish national archives, war diaries, veterans’ magazines,
photographs, and prisoner records, it offers a comprehensive view of World War II, facilitating
both large-scale and micro-historical research using Semantic Web technologies.
• arkivo [16, 17, 18, 19, 20] catalogues the extensive collection of archives, manuscripts, pho-
tographs, and artefacts held by the Józef Piłsudski Institute of America. By linking these resources
to Linked Open Data databases such as DBpedia [21] and Wikidata [22], the arkivo dataset
enriches the available information, facilitating further research into Polish history.
• Memorata Poetis [23] focuses on preserving and exploring ancient poetic traditions from Greek,
Latin, Italian, and Arabic literature. Its database incorporates semantic web features and is
enriched with Linked Open Data resources, such as Pleiades for geographic data and DBpedia for
various entities, providing enhanced tools for textual analysis.
By integrating and harmonising complex datasets, these initiatives provide rich data for exploring
historical and literary information.
3. The WOW Knowledge Graph
The WOW project’s KG captures the personal and professional relationships of Italian cinema divas from
their autobiographies. The following sections detail its development, starting with the source datasets.
We then describe the data model, which structures information to represent complex relationships.
Finally, we explain the workflow for populating the model, detailing how raw data was transformed
into RDF, linked to external datasets, and incorporated into the KG.
3.1. Source Datasets
The WOW KG uses data from three primary sources: divas’ autobiographies dataset, DBpedia, and
Wikidata. The divas’ autobiographies dataset has been developed from the annotations made by domain
experts, which catalogued 103 autobiographies written by 59 actresses, identifying 479 quoted passages
and 19 scholarly themes centred around 232 keywords. Additionally, 374 individual names mentioned
in the texts were annotated, along with their occurrences. This data was provided in CSV format.
From DBpedia, we obtained supplementary information on actress-writers and the individuals they
mention, as well as details about the films in which these actresses participated, including 448 film
entities. Additionally, we collected information on the professionals involved in these films, as well as
the actresses’ relatives and romantic partners, adding 1,049 names to the dataset. From Wikidata, we
incorporated user IDs for the divas across major social networks.
3.2. Data Model
The annotations were converted into RDF triples and integrated with other data sources into a cohesive
data model 1 . Concepts from the source datasets are described using metadata schemas [24], such as
DCMI Metadata Terms, and vocabulary models like SKOS and RDF Schema. Key entities and properties
were extensively reused, applying established reuse strategies [25] to promote interoperability and
reusability. Table 1 lists the imported ontologies and their namespace prefixes, while Figure 1 illustrates
the schema of classes, subclasses, and properties that comprise the data model.
Ontology Prefix Name Expansion
arkivo arkivo: http://purl.org/arkivo/ontology#
DBpedia dbo: http://dbpedia.org/ontology/
DBpedia dbp: http://dbpedia.org/property/
DCMI dcterms: http://purl.org/dc/terms/
DoCo doco: http://purl.org/spar/doco/
FOAF foaf: http://xmlns.com/foaf/0.1/
Pattern po: http://www.essepuntato.it/2008/12/pattern#
RDF Schema rdfs: http://www.w3.org/2000/01/rdf-schema#
Schema schema: http://schema.org/
SKOS skos: http://www.w3.org/2004/02/skos/core#
Table 1
Details on the re-used ontologies in the data model. It includes the ontology name, the corresponding prefix
used, and the expansion of each prefix into its full Internationalized Resource Identifier (IRI).
For clarity, our data model can be categorised into three main areas. The first area, bibliographic
and thematic, focuses on the autobiographies as literary works, incorporating bibliographic details and
thematic content. The second area addresses the professional aspects of the actresses’ lives, detailing
their film roles and career-related information. The third area relates the personal aspects, including
information about the actresses’ family and sentimental lives. Each of these areas is described in detail
below.
3.2.1. Bibliographic Data and Themes
The bibliographic data and classification system by subject and keyword form a key part of our dataset.
Autobiographies are catalogued on the basis of their first edition, with parameters such as title, publisher,
1
The data model is available at this link: https://github.com/AIMet-Lab/PRIN-WOW/blob/main/wow_schema.ttl. To produce a
human-readable form of the schema, the pyLODE documentation can be found here: https://aimet-lab.github.io/PRIN-WOW/
Figure 1: Schema of the WOW data model, illustrating the classes, properties and relationships.
year of publication, and genre (including several subcategories of the autobiography genre). These
data are supplemented by information on the publication status (whether still on the market or not),
represented by the schema:creativeWorkStatus property.
Excerpts from autobiographies, selected and analysed by scholars, are indexed in the WOW knowledge
base by page number (wow:nPage), stored into the doco:BlockQuotation class (imported from the
DoCO [26] ontology), and linked to the texts from which they were extracted through po:contains
and po:isContainedBy properties from the Pattern ontology2 . The content of each quoted passage
is classified by one or more pairs of terms formed by a theme (skos:Concept) and a keyword, e.g.
:Melancholy (theme) and :Loneliness (keyword), or :Body and :Mirror. Usually each keyword
belongs to only one theme, but rarely a keyword can belong to more than one theme: :Desire and
attraction can refer to both :Relationship with men and :Relationship with women
themes (as well as :Disappointment). The above mentioned non-hierarchical semantic relationship
between themes and keywords was implemented through the use of the skos:related property.
Names referenced in autobiographies are linked to the texts in which they are mentioned through
the arkivo:isMentionedIn property, which has been imported from the arkivo ontology. It is worth
noting the implementation of two further annotation properties not present in the above schema’s
depiction. These are: wow:nTimes, which enumerates the number of mentions of names in autobi-
ographies; and wow:artistAge, which notes the age of the divas at the time of the publication of the
autobiographies.
2
https://sparontologies.github.io/po/current/po.html
3.2.2. Filmographic Data and Divas’ Professional Lives
To gain insight into the professional lives of the actresses and delineate a timeline, we have started
collecting information on their year of debut (dbo:activeYearsStartYear) and the age of de-
but (wow:debutAge). We then gathered their filmographies from DBpedia: these included not only
performances as actresses (dbo:starring) but also roles as directors, producers, or screenwriters
(dbo:writer). After annotating these properties with the personal age of the divas at the time using the
wow:artistAge annotation property, we provided each film entity with information about genre and
release year (using the same properties as for the autobiographies). For each movie, we then gathered
information about cast, director, and producer: the retrieved names were incorporated into the WOW
dataset, along with the names of individuals referenced in the autobiographical texts. For each name
in the dataset, we then collected information about their occupations through the dbp:occupation
property. To date, the WOW dataset contains information on 448 films and an additional 1,116 names
of colleagues and others who were part of the actresses’ professional lives.
In the intersection between public and private, social networks provide valuable data, as noted
by experts. For each diva in the database, efforts were made to retrieve their account IDs on major
platforms (Facebook, Instagram, X). A search via Wikidata produced 36 results, recorded using the
foaf:account, opening opportunities for future research.
3.2.3. Divas’ Private Lives
In regard to the personal histories of the writing divas, domain experts provided data on their
dates of birth and death, which we supplemented with the corresponding geographical information
(dbo:birthPlace and dbo:deathPlace), as well as with birth names. A further attempt was made
to reconstruct the private lives of the divas by collecting data about parents, children, relatives, partners
and husbands. For each name collected, as before for names mentioned in texts or collected from
filmographic research, professions were then recorded. This kind of research presents a significant
challenge for domain experts, who are therefore highly interested in the results. To date, 64 entities
with different degrees of relatedness have been documented. However, this number is expected to grow
as more data from Wikidata is integrated.
3.3. Populating the Data Model
The names of individuals mentioned in the texts were extracted using Named Entity Recognition (NER)
and stored in CSV files. We then assigned semantic meaning to these names, including those of the writer
divas, through Named Entity Linking (NEL). This process generated the IRIs necessary for subsequent
searches. These IRIs were used to perform SPARQL queries against the DBpedia endpoint3 and to
query the Wikidata endpoint4 to obtain the social network account IDs of the divas. The individuals
representing persons, movies, and geographic locations are imported from DBpedia, as well as the
classes that contain them: dbo:Person, dbo:Film and dbo:Place. Actresses are further catalogued,
according to their profession, into the wow:Actress subclass of the dbo:Person class, and writer
divas into both the wow:Actress and the dbo:Writer subclasses. The doco:BlockQuotation
class and the skos:Concept class were imported and populated with individuals representing text
excerpts and their themes, while the wow:Autobiography class and the wow:Keyword class have
been implemented. Figure 2 illustrates a representation of the data transformation pipeline and the
connections established through NEL, as described above. The WOW dataset currently comprises
2,792 individuals, 1,266 of which are derived from annotations by domain experts and 1,526 have been
imported from external resources. The most relevant properties and their frequency are reported in
Table 2. The KG is available on Zenodo, with an associated citation [27], and is licensed with the open
Creative Commons BY 4.0 license.
3
https://dbpedia.org/sparql/
4
https://query.wikidata.org/sparql
Property Name Usage
dbp:occupation 1,929
dbo:starring 1,516
schema:keywords 1,435
schema:about 1,014
dbo:producer 292
dbo:director 237
Table 2
Usage count of main property linking class instances.
Figure 2: Graphical representation of the data flow process. The cylinders represent RDF data divided by class,
dashed arrows indicate NEL and data integration steps, while solid arrows indicate the population of classes.
In the initial phase, annotations were converted from CSV to RDF triples. Entities of the class dbo:Person
underwent NEL and data integration processes, while the classes dbo:Place and dbo:Film were populated
with data from DBpedia. All other classes were populated from the annotations.
4. Data Quality & Usefulness
The WOW knowledge graph is based on heterogeneous data that are considered high quality, as they
are ensured through multiple layers of validation and enrichment processes. First, domain experts
meticulously annotate autobiographical texts, identifying key themes, quoted passages, and references,
which are then transformed into RDF triples. This process leverages both manual and semi-automated
techniques to enhance accuracy. Additionally, integration with external datasets such as DBpedia
and Wikidata enriches the KG by providing supplementary information and establishing robust links
between entities.
In terms of fairness and data rights management, the knowledge graph leverages publicly available
information and datasets, including published autobiographies and data from Wikidata and DBpedia.
These sources are open and accessible, which supports transparency and reproducibility in research.
The validation and enrichment processes ensure that the data is accurate and representative, while the
use of open data adheres to principles of equitable access and responsible data usage.
The usefulness of the KG is evaluated based on its ability to facilitate meaningful research and insights.
Its Linked Data structure allows complex queries that reveal patterns and relationships not visible in
unstructured texts. For example, researchers can explore connections between Italian cinema divas’
autobiographies and their film roles. By integrating bibliographic details, filmographies, and personal
histories, the KG supports diverse research objectives and provides a comprehensive view of the subject.
5. Conclusion
The development of the presented KG marks an advancement in representing actresses’ autobiographies
within the humanities. By converting autobiographical texts into Linked Data and integrating them
with external datasets, this preliminary version of the KG offers a comprehensive, semantically rich
resource for examining the lives and careers of Italian cinema divas. However, it is important to note
that the KG is still a work in progress. As the dataset continues to evolve, efforts will focus on expanding
coverage and exploring additional research applications. Future developments aim to maximize the
graph’s impact by enriching the autobiographical data and its connections to broader cultural contexts.
Acknowledgments
This work has been supported by the PRIN 2022 project "WOmen Writing around the camera (WOW)"
funded by the European Union- Next Generation EU, Mission 4 Component C2, CUP: J53D23013480006.
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