In this paper, we present the GOLEM triple store, a massive triple store resource for ction and narrative. This triple store is the rst step towards a large-scale knowledge-graph for stories, as well as characters and events in narratives. At the moment, it contains more than 8 million stories collected from the Archive of Our Own (AO3) [1], providing scholars with a tool to derive unique insights into fan narratives and storytelling trends over time.
In this article we introduce a new resource for the large scale study of ction on the basis of
metadata and “derived data” [
The core concept of the GOLEM infrastructure is that of “programmable corpora”, i.e.
“research-oriented corpora providing an API” [
The rst batch of texts made available in the GOLEM triple store are gathered from the largest and most popular fanction platform, Archive of Our Own (AO3)2. The spread of digital and social media in the 21st century has recongured many literary dynamics, namely it has reduced the inuence of literary institutions like literary critics, publishers, and schools, creating more spaces and occasions for niche and amateur ction to become popular, thanks to reader-to-reader interactions. Fanction platforms and website for book reviews/discussion (e.g. Goodreads) are now some of the most thriving environments to study narrative, ction, and reader response. In the fanction space, readers become writers, viewers become creators and recipients become participants of their favorite (ctional) universes. Since fanction writers publish their own works independently from publishing houses and editors, their creativity knows no bounds, and is not subjected to limitations or censorship. Writers easily cross from one ctional universe (fandom) into another, or place themselves (or the reader) as characters in their favorite stories. Fanction has become an integral part of transformative fan culture, a cultural phenomenon in its own right.
Up to 2022 (the cut-o point of our data collection), more than 8.7 million stories were
published on AO3 in the English language alone. Additionally, there is a wide coverage of
other languages, including Chinese, Italian, or Korean including many so-called low-resource
languages, such as Bahasa Indonesian or isiZulu. Therefore, the fanction domain holds
immense potential for the study of user-produced narratives, readers’ response, semantic and
narrative modeling approaches, and for the development of natural language processing (NLP)
tools for low- or under-resourced languages. While certain individual features of fanction
domain have been investigated, such as user-selected and user-provided tags [
The GOLEM Project triple store is a large and easily accessible resource for querying AO3 data and more data sources will be added later on. We demonstrate the potential of the triple store in three case studies.
The article is structured as follows: Section 2 describes the data and its representation in the triple store. Section 3 presents some illustrative case studies. Section 4 contains the discussion, and Section 5 describes future work and planned extensions.
Apart from the textual data provided by fanction writers and the common metadata such as author and title, AO3 provides a wide array of additional metadata, such as user-selected content tags, characters appearing in the story, as well as their relationships. Particularly popular are romantic (canon and non-canon) character pairings. Users can praise and react to each other’s work by giving kudos or leaving comments.
Individual stories in the triple store are identied by their story ID. Each story has a number
of associated metadata items, such as summary, word count, date published and more. As of
date, all predicates in the triple store are using the golem prex (https://golemlab.eu/graph/ ),
derived in parts by properties from CIDOC-CRM [
For internal use, the data was rst harvested from the archive.org archive3 and stored in an internal Elasticsearch database with help of a custom ingest script4. This database has now been converted into triple store data and is available at: http://graph.golemlab.eu:8890/sparql via an institutional Virtuoso server5. Virtuoso was chosen because it scales well with growing knowledge graphs. Even holding multiple billions of triples on a single instance, single machine setup, Virtuoso still performs well, in a real-life setting.6.
Up to the date of this publication, metadata for 8 million stories have been made available. The data in the GOLEM triple store contains the story related metadata in AO3 up to and including December 2022. With this choice we want to limit the stories that are potentially written with the aid of large language models, allowing for a more reliable investigation of human storytelling. While comparing human-generated stories with narratives produced by large language models could be an interesting area of research, it is not currently within the scope of the project. Extending the knowledge graph with more recent (human) user-produced stories from AO3 and other fanction platforms is planned for future updates. 3https://archive.org/details/AO3_nal_location 4Available at https://github.com/GOLEM-lab/golem-ingest 5https://virtuoso.openlinksw.com/ 6See UniProt https://www.w3.org/wiki/LargeTripleStores#OpenLink_Virtuoso_v7.2B_.2894.2B.2B_explicit.2C_ uncounted_virtual.2Finferred.2C_in_1_instance_on_1_machine.29
archiveofourown.org
We transferred the Elasticsearch data into triple store in a series of steps. Firstly, the database was queried for stories by languages other than English. The smaller language sets were converted to triples in one step. Larger languages, such as Russian and Chinese, were processed using a batch size of 50,000 stories. The English data was queried from the database by fandom. The larger fandoms were processed in batches, while the smaller fandoms, i.e. the fandoms with few or very few stories, where queried sequentially from the database, before they were converted into triples according to the schema above.
This process has two time-consuming bottle necks: the download and the import into the Virtuoso instance. This is illustrated on the example of English fanction stories for the Attack on Titan anime in Table 2. The Elasticsearch data (jsonl format) for this fandom has a size of 4.9 GB, resulting in 60,503 triple store entries.
Three small case studies are presented here to demonstrate how to use the triple store and give more insights into the data contained in it. First, to know which languages are contained in the data and how many stories per language there are (stories can have more than one associated language), we can write a simple SPARQL query using COUNT. The same query can be made for dierent fandoms, yielding a ordered list of fandoms with the most stories (i.e. Harry Potter J.K. Rowling: 324,767, Marvel Cinematic Universe: 252,605, Supernatural: 244,182). 1 PREFIX golem : <h t t p s : / / g o l e m l a b . eu / graph/> 2 SELECT ? o (COUNT( ? o ) a s ? oCount ) WHERE 3 { 4 ? s golem : l a n g u a g e ? o . 5 } 6 7 GROUP BY ? o 8 ORDER BY DESC( ? oCount )
The results yields a list of 110 languages in total, with the top 10 by story count presented in Table 3.
Next, to nd out the distribution of stories per rating (e.g. Mature or Explicit) for the fandom “Artemis Fowl - Eoin Colfer”, we can use the following query: ? s golem : r a t i n g ? o . ? s golem : fandom " A r t e m i s ␣ Fowl ␣ - ␣ Eoin ␣ C o l f e r " .
It yields a distribution of ratings of stories within the fandom, which is normalized and illustrated in Figure 2. We can see that this particular fandom produces stories that are largely targeted at general audiences or teen and older audiences, with only ew explicit or mature stories. In contrast, the same query for the fandom BTS (a popular Korean boy band) produces a dierent distribution, with a larger proportion of explicit and mature stories (see Figure 3).
Content-related elds are interesting for processing the fanction data in downstream tasks, and to derive additional semantic information on the stories. Therefore, the last example shows how to query for a list of summaries of fanction stories in a specic fandom and language that are tagged with a specic keyword. p r e f i x golem : < h t t p s : / / g o l e m l a b . eu / g r a p h / > SELECT ? o WHERE { ? s golem : keyword " A n g s t " . ? s golem : fandom " A t t a c k on T i t a n " . ? s golem : l a n g u a g e " E n g l i s h " .
? s golem : summary ? o . }
The result of this query are available at https://github.com/GOLEM-lab/triple_store/.
In this short paper, we present the GOLEM triple store, our eort towards a comprehensive semantic representation of fannish narratives. It provides users with manifold possibilities to study fanction from dierent viewpoints, e.g. by inspecting keywords and tags provided by the users or the distribution of romantic pairings across dierent fandoms. To date, the triple store contains more than 8 million stories. An overview on the statistics of the GOLEM triple store is given in Table 4.
(Stories by) Orphaned Accounts (Stories by) Anonymous Accounts
The presented triple store is the rst step towards a broader knowledge base for fanction
narratives. In the short term, the triple store will be extended with additional reader response
data, such as number of time users have bookmarked a story. It will further be extended from
a story-centric view to a more complete data modelling based on the existing AO3 metadata,
e.g. by modelling content collections according to various criteria. In the medium term, the
GOLEM triple store will be extended towards a full-edged knowledge graph of characters
and events in the fanction domain. This includes the results of character analysis, modeling
essential properties of ctional characters, i.e. physiological and psychological traits, as well
as narrative function of a character. Additionally, the full knowledge graph will also contain
additional data on reader response (e.g. emotions felt, etc.) The project is currently developing
a comprehensible ontology [
We are additionally planning to report recent statistics of the quality of the knowledge graph (such as consistency) regularly on the project website.
Currently, the triple store only contains stories from AO3. However, we are working on including data from other sources, such as Wattpad7 and fanction.net8.
This work is part of the Golem Lab: Graphs and Ontologies for Literary Evolution Models project, a 5-year (2023-2027) research project funded by the European Commission (ERC StG).