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Prosopographic Modelling - Introduction

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3 minutes

Prosopographical data in the semantic web

In the following we introduce the basic terminology in relation to data modelling and semantic web.

Main concepts


  • In the context of Digital Humanities, an Ontology is mostly defined as “a formal, explicit specification of a shared conceptualization.” (Studer et al, 1998)
  • An ontology is an explicit model of concepts, which are defined with clear semantics.
  • It is important that users of an ontology understand the underlying assumptions about the chosen domain.
  • Normally an ontology is expressed as a set of classes and properties representing concepts and their relationships.

RDF - Resource Description Framework


  • abstract data model to state anything about anything by anybody

  • URIs to identify entities

  • triples: <subject predicate object> to form a graph

  • named graphs: allows to assign a fourth identifier to a group of triples (quad mode) and use that to further describe the “named graph”.

    @prefix cidoc: <> .
    @prefix dct: <> .
    @prefix ns1: <> .
    @prefix rdfs: <> .
    @prefix void: <> .
    ns1:provenance {
        <> a void:Dataset ;
            dct:created "2021-06-02"^^xsd:date ;
            dct:creator "Matthias Schlögl, Peter Andorfer"@de ;}
    <> {
        <> a cidoc:E69_Death ;
        rdfs:label "Death of Wolf Pickl"@de ;

OWL - Web Ontology Language

SKOS - Simple Knowledge Organisation System

  • (2009)
  • schema for modelling controlled vocabularies (such as taxonomies, thesauri, etc.) in a way that is compliant to Linked Open Data.
  • is based on concepts (terms) which can be supplemented with labels, notes, comments, etc.
  • SKOS concepts can be linked to each other using hierarchical or associative relations
  • can also be linked to other vocabularies

SPARQL- SPARQL Protocol And RDF Query Language

LOD - Linked Open Data



Description of sample datasets

See the presentation slides.


  • Project: The Viennese Court – A Prosopographical Portal (ÖAW Innovationsfond 2020 - 2022)
  • Data model: Event-based, basic entities + typed temporalized relations between them
  • currently 12905 Persons, 1398 Types, 376 Persons


  • Project: Nuns and Monks - Prosopographical Interfaces (ÖAW go!digital Next Generation 2019-2021)
  • Data model: Factoid-based (Document Interpretation Act => Aspects)
  • currently 465 Persons, 429 Religious titles, 1692 Document Interpretation acts



  • database for RDF
  • graph as underlying data model



  • framework for managing and publishing prosopographical data
  • implemented in Python with Postgresql DB as persistence layer
  • RDF-serialisation exposed via API


ResearchSpace -

  • semantic knowledge platform
  • Java application with predefined frontend-components and a flexible templating mechanism operating directly on SPARQL endpoint to retrieve and present data

SPARQL basics

  • Query language for the semantic web
  • since 2008 W3C recommendation
  • since 2013 SPARQL 1.1 W3C recommendation

The anatomy of a SPARQL query

Following illustration describes the different parts of a SPARQL query.

The anatomy of a SPARQL query

  • Prefixes: optional, used to shorten URIs if they make query difficult to read if spelled out in full. Use prefix in query
  • Define dataset: optionally restrict the graph from which to select
  • Query result clause: which variables should be returned in the result
  • Query pattern: formulate filtering conditions
  • Query modifiers: modify and specify your query further (eg. group results by a particular variable, limit query, etc.)
  • Variables are freely chosen names prefixed by ?

Four types of SPARQL queries

  • SELECT : returns values based on some query pattern
  • CONSTRUCT : creates a new graph consisting of new triples
  • ASK : returns a yes/no answer, e.g. do any entities with given property exist
  • DESCRIBE : provides information about a resource, without explicitly stating the query pattern. The query processor determines the result set.

Four types of SPARQL queries

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