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Prosopographic Modelling - Introduction

Geschrieben von
Matej Ďurčo
Veröffentlicht am
10. Juni 2021
Getagged mit
Prosopographic data und Semantic web

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

  • https://www.w3.org/RDF/

  • 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: <http://www.cidoc-crm.org/cidoc-crm/> .
    @prefix dct: <http://purl.org/dc/terms/> .
    @prefix ns1: <https://omnipot.acdh.oeaw.ac.at/> .
    @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
    @prefix void: <http://rdfs.org/ns/void#> .
    ns1:provenance {
        <https://viecpro.acdh-dev.oeaw.ac.at/entities#> a void:Dataset ;
            dct:created "2021-06-02"^^xsd:date ;
            dct:creator "Matthias Schlögl, Peter Andorfer"@de ;}
    <https://viecpro.acdh-dev.oeaw.ac.at/entities#> {
        <https://viecpro.acdh.oeaw.ac.at/entity/100008/death> a cidoc:E69_Death ;
        rdfs:label "Death of Wolf Pickl"@de ;

OWL - Web Ontology Language

SKOS - Simple Knowledge Organisation System

  • https://www.w3.org/TR/skos-reference/ (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)
  • https://nampi.icar-us.eu/
  • https://github.com/nam-pi
  • 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 - https://www.researchspace.org/

  • 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

Video resources: