Our starting point is tens of thousands of Garzoni contracts and our finishing line is an information system to make sense of all the information they contain. As outlined in a previous post, the route between the two is composed of several steps, one of the first being the design of a data model.
What is a data model?
Roughly speaking, the definition of a data model corresponds to the identification of what is of interest in the ‘world’ — in our case Garzoni contracts — we are looking at. Regardless of the format, the role of a model is to represent the structure and the main elements of a ‘world’ in order to explain and/or dynamically reproduce it. As a result, a model should enable to answer questions or solve issues regarding the modeled world. The modelling step is thus a crucial one: it establishes the work-space and prepares what can be done.
How to proceed?
To represent a portion of reality we need to identify its concepts, its entities, and their interactions. In the context of Garzoni, we are quite fortunate for the content of the contracts — recall it is administrative documents — is extremely regular, with always the same pieces of information. Before starting off with the definition of our vocabulary, we first had to address two issues: the choice of a formalism and the characterization of the scope of the model.
Regarding knowledge representation formalism, we chose to express our model through an ontology, using the OWL language. This choice is motivated, among others, by the wish to (a) exploit inference capacities, (b) re-use domain knowledge, (c) benefit from the technical eco-system of the Semantic Web and (d) integrate and share our data according to linked data principles.
As for characterizing the scope of our model, a pre-study of the documents coupled with the consideration of historical research questions at stake in the Garzoni project allowed us to formulate competency questions, that is to say questions that we wish the knowledge base could answer. In the context of Garzoni, research questions revolve around economical aspects (e.g. how did apprentices’ salaries differed according to professions, guilds, time, etc.), knowledge transmission (e.g. were apprentices just a source of labor or were they trained), migration flows (e.g. where did apprentice come from), family and gender issues (e.g. at what age boys and girls were sent away to become apprentice), and others. Each question can be looked at from a veneto-Venetian view point, or put into the larger European perspective. As a side remark on this point, the Garzoni project would wish, on the long term, to cross data-sets and studies with projects of similar veins.
At this point it is worth having in mind that an ontology is inherently relative: it corresponds to one of many possible views of a domain (which differ according to time, cultural background, targeted application, etc.) and it evolves through time. Ours is naturally shaped according to our needs, and is iteratively revisited.
The Garzoni data model
Event though the model might evolve further in the future, it can be seen as fairly stable and complete as of today. Our intention here is not to detail the whole ontology but rather to present its main building blocks and emphasize some points of interest.
The Garzoni data model covers different areas of information:
- information about the sources (archival dimension): a garzoni contract (a document) is materialized on two mediums: the real paper registers on the one hand, theirs scans on the other. For both mediums, the ontology represents appropriate descriptive and technical metadata information. Fig. 1 below illustrate this part of the ontology; further aspects about the digitization process are described using the CRM-dig ontology (not represented in the figure).
- information about persons (people dimension): garzoni contracts feature different « actors », with different roles (Apprentice, Master and Guarantor) and characteristics (please see an example in this post). As mentioned before, Garzoni contracts are quite consistent and the mention of a person follows more or less the same pattern, with the indication of his/her name, age, profession, residence, geographical origins and potentially his/her charge (a kind of public responsibility, different from the profession). These attributes are however not evenly distributed: the age is mentioned for the Apprentices exclusively, while charges are often indicated for Guarantors. The ontology has classes and properties to represent all information about persons.
- information about various entities or concepts: besides Person, contracts mention Places (sometimes with the indication of the Sestriere — a Venetian district — and of the Parish), Workshops (with their insigna) and Professions.
- information about contracts’ terms (condition dimension): each contract gives information about financial conditions and what we called « hosting » conditions. The first one relate to the salary and/or the pledge which were due by one of the parties. Salaries can be quite complex, with different amounts paid by stages. Hosting conditions relate to various aspects of life which where sometimes supported during the apprenticeship: the general expenses of the apprentice, his/her clothing, personal care and accommodation. Financial and hosting conditions are defined classes in the ontology, made up of some condition components (salary, accommodation, pledge, clothing, etc.).
- information about events: garzoni contracts give evidence of the occurrence of several events. The apprenticeship itself, its enrollment at the Giustizia Vecchia, the potential breach of the contract as well as the flee of the apprenticeship (and, sometimes, his return). To represent this information, we make use of classes and properties of the Simple Event Model (SEM) ontology.
- information about relations, which can occur between contracts (a contract cites another one) or between persons (mainly family and business relations).
All this information form the core of the content of Garzoni data, which we do not detail further here. Instead, we now turn our attention to the questions of (1) how to represent information which belong to different levels, (2) how to represent information which evolves through time and is dependent on a specific view point, and (3) how to trace its provenance.
Mentions and Entities – When dealing with textual data, a obvious aspect to model is the difference between what appears in the text (the word) and what it is being referred to (the ‘thing’, through the meaning). In the case of lexical information, this amounts to distinguish between an occurrence of a word in a text and its meaning as stated in a dictionary. When dealing with entities, this distinction corresponds to the mention(s) of an entity in texts on the one hand and, on the other, the entity of the world they refer to. The former is used to represent the various possible occurrences or mentions of e.g. Francesco de Philippo in different contracts, and the latter to represent the unique person of Francesco de Philippo who lived at a certain time. The link between the two classes is reified, in order to represent meta-information about the relationship itself, especially which agent led to its creation, a human or a machine. The Figure below displays the organization of these classes. In the future, this will allow an informed exploitation of the data.
Statements – In our model, entities (not mentions) are objects which collect information about different states of existence. No matter how this ‘snapshots’ will be collected, they all originate from a given source and assert some properties valid at a certain time. Without considering here all the options possible to encode this information, let us just mention our choice, that is to say the usage of Statements similarly as in the RDF representation of Wikidata (Vrandecic, 2014). Each entity has a set of property-value pairs, whose values are not the value itself, but a Statement giving access to the value, its validity time, and its source or reference. If the entity Francesco de Philippo appears in two contracts, first as a Master, second as a Guarantor, it will have two role statements, asserting that this person was a Master at a certain date according to a certain source (a contract), and a Guarantor at another date according to another source.
Provenance – Garzoni data is acquired through a web-interface (cf. section 5) whose internal model relies entirely on the Open Annotation Data Model (OA). Each and every instance correspond to an annotation which relates a target (what is annotated) and a body (the content of the annotation) and is further qualified by the motivation of the annotation and the agent who created it. Based on this input, the Garzoni RDF data-set will naturally keep this provenance information.
Auteur: M. Ehrmann
[Vrandecic 2014] Vrandecic, D. and Krötzsch, M. Wikidata: A free collaborative knowledge base. Commun. ACM, 57(10):78–85.