This contribution presents the GOLEM Knowledge Graph and interface, ofering diferent perspectives into content-related data and metadata from the domain of fanfiction narratives. The Knowledge Graph is aligned with common ontologies and vocabularies from the domains of narrative and cultural heritage. In this short paper, we outline how narrative organization and characters' features are modelled in the GOLEM knowledge graph. The GOLEM UI is also presented, a user-friendly access point to the data that allows to browse the knowledge graph even without knowledge of SPARQL.
a specific fandom, fanfiction works are polyvocal interpretations of shared stories, internet folklore that is produced at a growing pac1e1[]. The works are polyvocal in the sense that, through a work, reader and writer are in dialogue with a canonical narrative universe that inspired it, but they are also uniquely engaged with each other. Not only in the traditional cycle of production-reception, but also through active and immediate engagement via comments or other interactions (e.g. kudos). This engagement can be measured and subsequently used to study the evolution of (cultural) traits of literary works, e.g. the appearance (and disappearance) of certain character traits, or the change in character roles (villain-to-hero, enemy-tolover, secondary-to-main character).
In order to make these valuable data more easily accessible to interested researchers and
other stakeholders, we created the GOLEM-UI, an easy-to-use interface based on the SAMPO
framework [
In the knowledge graph, a subset of the triple store data has been modelled. Fanfiction
stories and canonical works are represented as instances lormf:F1_Work [
The second main aspect to be modelled is that of social relations. In fanfiction, romantic or
sexual pairings are expressed through “Character/Character”-relationships (so-called
“slashing”). This category is of special interest, because it allows users to investigate the recurrent
use of gender-specific features and character pairings, as well as their influence on the
popularity of a story both within a specific fandom and across fandoms in comparison with the
canonical relationships1[
Additional metadata are modelled wiDthCMI Metadata Terms4, e.g. dct:title ordct:creator. At
the moment, domain-specific terms, such as the number of kudos – a form of user-interaction
similar to ‘likes’ – are modelled using project-specific categories, e.ggo. lem:numberOfKudos.
This will be updated in the future once the KG makes full use of the GOLEM ontology. For
copyright reasons, the KG (as well as the triple store) does not provide access to the fulltexts
of the fanfiction stories. This is consistent with the goal of a large scale study of fiction using
derived features 9[
SAMPO ofers a framework to create diferent focused perspectives on the data. The queries are predefined. Therefore, end-users do not need to be proficient in SPARQL. Table1 gives an overview of the current state of the KG, while Figur1eprovides a breakdown of stories by language. 1. Metadata view: Overview of the stories’ metadata, 2. Fandoms view: Statistics on fandoms and associated stories, 3. Characters view: A closer look into characters and their various romantic pairings, for now with a special focus on theHarry Potter fandom (Potterverse), 4. Literary quality view: Statistics on readability and literary quality of the stories.
The metadata view gives an overview over the common properties of the stories, such as the word count, the language of the work, or the associated fandom (Figu3r).e Facets on the left hand side of the search perspective allow users to filter data by language and fandom.
The fandoms view gives statistical overview over the representation of specific narrative universes, e.g. how many stories are associated with the respective fandom (Figu4r)e. Fandoms are represented by theirskos:prefLabel, which in some cases can be a translation of the title. Sub-fandoms are connected to their more general fandom (e.Hg.arry Potter and the Philosopher’s Stone to the fandomHarry Potter - J.K. Rowling) by skos:broader/skos:narrower relations.
The characters view shows a character’s aliases (e.gV.oldemort, Lord Voldemort, Tom Riddle and so on), keywords that are associated to a certain character (i.e. all the possible variations a character can have), and the number of stories a specific version of the character appears in. In order to link between them all the versions of a character, we introduce the clGas0s_Character-Stof a crm:E28_Conceptual_Object. This represents the general idea of a character, e.gV.oldemort. Specific realisations of a character, e.g.Voldemort in Harry Potter and the Half Blood Prince, are represented as individuals oGf1_Character a crm:E89_Propositional_Object.
The GUI shows characters and their associated synonyms from thPeotterverse fandom, as well as the number of social relationships in which a character is involved. This statistic gives us an insight into which characters are popular love-interests in a narrative universe or with whom they interact more frequently. For instance, Figur5eshows that currentl5y, the character of Albus Severus Potter has several alternate names used by authors to refer to him, he is involved in 30 slash (romantic and/or erotic) relationships, and most commonly (17 times) with the characterScorpius Malfoy. 5Date of the submission: 2024-10-15.
Lastly, we calculated scores modelling the literary quality (and readability) of the texts
associated to the stories in the knowledge grap6h.These measures include: Flesch Reading Ease,
Flesch-Kincaid Grade Level, SMOG Readability Formula, Automated Readability Index, New
Dale–Chall Readability Formula, for readability; and sentence-length, type-token ratio, and
compressibility, for stylistic complexity (adapted fro2m]).[ Figure7 shows an overview of the
available data fields in the literary quality view. In Figur6e, we illustrate how this extracted
features can be combined with other information for data exploration, e.g. looking at the
association of individual measures of literary quality with reader response. Kudos is the reader’s
appreciation measure used on AO3 and it seems to be positively correlated with the average
word entropy of stories [cfr8.].
6We adopt the termliterary quality from [
While the triple store1[0] remains available athttp://graph.golemlab.eu:8890/spar q,lthe knowledge graph can be queried via the same endpoint and the designated graph usiWngITH GRAPH <http://golemlab.eu/graph/>.
For example, the following query will yield all the stories in the knowledge graph with their respective authors (anonymised) and the romantic category (e.g. F/M-relationships): p r e f i x golem : < h t t p : / / g o l e m l a b . eug/ raph / > WITH GRAPH < h t t p : / / g o l e m l a b . eu /graph / > SELECT ∗ WHERE { ? s d c t e r m s : t i t l e ? t i t l e . ? s d c t e r m s : c r e a t o r ? a u t h o r .
? s golem : r o m a n t i c C a t e g o r y ? c a t e g o r y . } The result of this query can be found atthis link.
The GOLEM-UI is an intuitive interface that allows diferent insights into the GOLEM knowledge graph. Four diferent search perspectives allow users who are not familiar with SPARQL queries to gain insights into the data modelled from the domain of fanfiction stories. Four predefined perspective are currently available: Metadata, Fandoms, Characters, and Literary Quality. This interface, together with the possibility of exporting the results of queries in CSV format will allow researchers to easily create corpora that they can use for their analyses of the online production and reception of fiction.
The presented user interface is one step towards presenting the full-fledged GOLEM
ontology, which will include many more features than those presented here. For instance, it will
allow modeling events and event chains according to diferent narrative theories, e.g. the
hylistic approach [
The authors would like to thank Heikki Rantala for his helpful comments and support in creating the GOLEM-UI. This work is part of theGraphs and Ontologies for Literary Evolution Models (GOLEM) project funded by the European Commission.