By the end of this section, you should be able to:
- Describe some of the existing formal ontologies that are most relevant to (digital) Humanities
- Understand and explain the difference between top-level ontologies, and more focussed ontologies
- Know how to find the most relevant ontology model for your research
What formal ontologies exist that are relevant to digital humanities?
The potential topics of interest in digital humanities are vast and potentially unlimited, so it is not really possible to state what formal ontologies may be of use in any given research project. The appropriateness of an ontology for application in a particular research programme depends on the data and information you want to include in it, and the research question brought to it. Your research question, and the kind of data you want to model should be compared against the scope of potential ontologies in order to see whether the proposed ontology standard is suitable.
It is of interest, however, to point out the distinction between top-level ontologies and domain or application ontologies. Top-level ontologies allow integration of data on an extremely high level, sometimes including logical rules within the structure that allow for automated reasoning over datasets. Some of the more well known top-level ontologies include:
- Basic Formal Ontology: originally used in modelling of medical data, presents a complete methodology for data modelling
- DOLCE: was constituted to aid in modelling common sense notions arising from natural language
- CIDOC CRM: originally designed in the museological community, it has been broadened to account for cultural heritage and e-sciences data
On the other hand, other ontologies are designed to address very specific modelling issues, ignoring the general aim of interoperability in favour of organizing a smaller research/problem space. Examples of such focussed ontologies include:
- FOAF: an ontology for tracking social relations
- SPAR: for organizing citation data, article structure and context
- NeMO: for tracking scholarly process
Unfortunately, there is no single collection of ontology resources that would allow the perusal of all potentially applicable ontologies. Sites such as schema.org and bartoc.org however do provide tools to find potential specific schema and ontology representations that may suit your needs.
EXERCISE TO TRY OUT QUERYING AN ONTOLOGY
“BARTOC.org | Basel Register of Thesauri, Ontologies & Classifications.” n.d. Accessed January 23, 2018. http://bartoc.org/.
“Basic Formal Ontology (BFO) | Home.” n.d. Accessed January 23, 2018. http://ifomis.uni-saarland.de/bfo/.
“FOAF Vocabulary Specification.” n.d. Accessed January 23, 2018. http://xmlns.com/foaf/spec/.
“Home | CIDOC CRM.” n.d. Accessed January 23, 2018. http://www.cidoc-crm.org/.
“Home – Schema.org.” n.d. Accessed January 23, 2018. http://schema.org/.
“Laboratory for Applied Ontology – DOLCE.” n.d. Accessed January 23, 2018. http://www.loa.istc.cnr.it/old/DOLCE.html.
“Linked Open Vocabularies (LOV).” n.d. Accessed January 23, 2018. http://lov.okfn.org/dataset/lov/.
“NeDiMAH Methods Ontology: NeMO.” n.d. Accessed January 23, 2018. /content/nedimah-methods-ontology-nemo.
“SPAR Ontologies – Home.” n.d. Accessed January 23, 2018. http://www.sparontologies.net/.
Your progress through the “Formal Ontologies: A Novice’s Guide” module