ARCHIVED

Knowledge Engineering in the Digital Humanities

Wissensrepräsentation und -verarbeitung in den DH

Institution: University of GrazProgramme: Master's Programme in Digital HumanitiesFormat: University course20214 Sessions90 Minuten

The course introduced knowledge representation for Digital Humanities projects. It combined RDF graph modelling, ontology engineering, SPARQL-based exploration and the critical reuse of existing vocabularies and authority data.

Instructor: Dr. Christopher Pollin

Contact: christopher.pollin@dhcraft.org

Material status

This page reconstructs four Knowledge Engineering units from related, verified teaching materials. The checked sources do not preserve a direct historical session record for this exact sequence. The supplementary RDF Basics chapter is a draft, and some secondary archive paths may be outdated.

Learning Outcomes

  • Distinguish conceptual models, vocabularies, ontologies and knowledge graphs
  • Represent humanities entities and relations as RDF
  • Design a domain ontology and document modelling decisions
  • Query and evaluate knowledge graphs with SPARQL
  • Reuse established vocabularies and authority data in a research model

Prerequisites

  • Basic knowledge of Digital Humanities
  • Basic understanding of data modelling

Sessions

1
SESSION 1

Knowledge representation and graph modelling

Concepts, entities and relations as explicit components of a research model.

Topics
  • Knowledge representation
  • Graph models
  • RDF and RDFS
2
SESSION 2

Ontology engineering

Developing classes, properties and constraints for a humanities domain.

Topics
  • Ontology design
  • OWL
  • Modelling patterns
3
SESSION 3

SPARQL and knowledge-graph exploration

Querying graph structures and testing whether the model supports research questions.

Topics
  • SPARQL
  • Triple stores
  • Query evaluation
4
SESSION 4

Applying and evaluating a domain model

Connecting research data to reusable vocabularies and evaluating modelling choices.

Topics
  • Vocabulary reuse
  • Authority data
  • Model documentation and evaluation

Resources

Essential Tools

Recommended Reading

Online Resources