Ontopavese
The Ontology
OntoPavese in a nutshell
The formal ontology OntoPavese aims to represent the works of Cesare Pavese in both breadth and depth by structuring the available knowledge within a rigorous semantic hierarchy. It encompasses all published works, both poetry and prose, as well as private documents such as letters and diaries, and archival data. In this way, it builds a coherent network of bibliographic, philological, and archival information aligned, on one side, with the IFLA Library Reference Model (LRM) and its related formal ontology, LRMoo, now incorporated into CIDOC-CRM, and, on the other side, with the Records in Contexts (RiC) model developed by the ICA and its ontology, RiC-O.
At present, the dataset includes the complete primary bibliography of works written by Pavese, along with the major re-editions. For the most significant works, information is available on individual poems, chapters, and dialogues, including details on language, translations, sections, pages, dates of creation, and links to the digital annotated edition of Pavese In Testo.
Letters, diary pages, and essays are also modeled based on data extracted from XML-TEI encodings, in their current project state (February 2026), with metadata on author, related work, language, place, creation time spans, sender, and recipient.
Completing the framework are archival data concerning the handwritten drafts of the poems from Lavorare Stanca (1936), including signatures, location, and versions.
OntoPavese not only makes this data accessible but also enables researchers to query it through a search tool designed to reveal new connections and inspire new questions.
What is a formal ontology?
To understand how OntoPavese makes it possible to bring out new connections and new questions, it is necessary to clarify what is meant by formal ontology. In the field of computer science, this term refers to a representation of a given domain of knowledge (in our case, Pavese’s work) through the entities that belong to it, along with their characteristics and the relationships that link them. More precisely, an ontology allows us to define the type of an object (the class to which it belongs) and its properties. For example, among the classes that might be useful for OntoPavese are publishing houses and the books they publish. And since what a publishing house does is precisely to publish books, we can say that to publish is a property associated with both classes.
When this schema is “populated” (that is, filled) with data about the specific individuals we want to describe (for example, the editions of Pavese’s works), it becomes a knowledge graph: a network in which each element is connected to others through relationships. In a knowledge graph, all information takes the form of a triple: subject, predicate, and object (formally represented in the descriptive language RDF).
In the image below, we have a representation of the basic ontology just described, with the class of publishing houses and that of books connected through the property “publishes.”
We can then populate the ontology, for example, by adding Einaudi, an individual belonging to the class of publishing houses, and Lavorare stanca, an individual belonging to the class of books.
Both the subject (Einaudi) and the object (Lavorare stanca) can then be involved in other triples, within a vast network of relationships.
But let us now consider why one should use a representation of this kind instead of a more classical database organized in rows and columns.
The answer is that an ontology offers numerous advantages; to mention just a few:
- It can be easily integrated with new information.
- It handles incomplete information effortlessly. If the headquarters of a publishing house is not known, one simply does not enter it.
- It makes it possible to deduce implicit information. Ontologies can be queried with “inference engines” that make it possible—by defining rules (logical axioms)—to discover new knowledge by making implicit data explicit.
Beyond these operational features, however, the most important aspect of ontologies lies in their ability to connect with other existing information, thereby contributing to what is known as Open Science.
Indeed, every entity in an ontology is uniquely associated with an internet address (an IRI, Internationalized Resource Identifier). This characteristic, together with the fact that it can reference other ontologies that are standards in the fields of cultural heritage, archives, and literary resources, makes it possible to place OntoPavese within a broader existing network of information: the Semantic Web. This is an ecosystem of web models and standards in which resources are described and linked to one another in a formal way so that they can be processed by automated systems. By way of example, here are some datasets that are part of the Semantic Web: DBpedia, which contains Wikipedia data in ontological form; Europeana, with millions of digitized books, paintings, films, and documents from European museums, archives, and libraries; GeoNames, with over 25 million names and geographic locations; dati.camera.it, containing data on members of parliament and legislative activity of the Italian Chamber of Deputies since 1848.
In other words, an ontology is not merely a technical tool for organizing data, but a conceptual infrastructure that makes information connected, expandable, and reusable over time. This ability to link different levels—texts, documents, people, places, organizations—and to integrate them with external resources transforms a simple digital archive into a true research environment.
OntoPavese is, ultimately, a digital map of Pavese’s universe: an invisible yet essential structure that turns a heritage of documents and scholarship into an explorable, queryable, and ever-evolving network of knowledge.
OntoPavese's documentation
[Coming soon…]
Examples of queries in OntoPavese
How to visualize OntoPavese in GraphDB
