A Formal ontology
is an ontology
with a structure that is guided and defined through axioms
. The goal of a formal ontology is to provide an unbiased
- and application-independent) view on reality
. Formal ontologies are founded upon a specific Formal Upper Level Ontology
, which provides consistency
checks for the entire ontology and, if applied properly, allows the modeler to avoid possibly erroneous ontological assumptions encountered in modeling large-scale ontologies.
By maintaining an independent view on reality the ontology gains the following properties:
- indefinite expandability:
- :the ontology remains consistent with increasing content.
- content and context independence:
- :any kind of 'concept' can find its place.
- accommodate different levels of granularity.
Theories on how to conceptualize reality date back as far as Aristotle.
Existing Formal Upper Level Ontologies (Foundational Ontologies)
Common Terms in Formal Ontologies
The Difference in terminology used between separate Formal upper level ontologies can be quite substantial, but the one and foremost Dichotomy
most Formal upper level ontologies apply is that between 'Endurants' and 'Perdurants'.
Also known as continuant, or in some cases 'substance'.
Endurants are those entities
that can be observed-perceived as a complete concept, at no matter which given Snapshot
Were we to freeze time we would still be able to perceive/conceive the entire endurant.
Examples are material objects, such as an apple or a human, and abstract 'fiat' objects, such as an organisation or the border of a country.
Also known as occurrent, accident or happening.
Perdurants are those entities for which only a part exists if we look at them at any given snapshot in time.
When we freeze time we can only see a part of the perdurant. Perdurants are often what we know as processes, for example 'running'. If we freeze time then we only see a part of the running, without any previous knowledge one might not even be able to determine the actual process as being a process of running. Other examples include an activation, a kiss, or a Procedure
In a broad sense, qualities can also be known as Property
Qualities do not exist on their own, they need another Entity
(in many formal ontologies this entity is restricted to be an endurant) in which they resume. Examples of qualities and the values they assume are colors (red colour), or temperatures (warm).
Most formal upper level ontologies recognize qualities or something related, although the exact classification may differ. Some see qualities and the values they can assume (sometimes called quale) as a separate Hierarchy
besides endurant & perdurant (example: DOLCE). Others classify qualities as a subsection of endurants, e.g. the dependent endurants (example: BFO).
Formal versus NonFormal
An ontology might contain a concept representing 'mobility of the arm'. In a nonformal ontology a concept like this can often be classified as for example a 'finding of the arm', right next to other concepts such as 'bruising of the arm'. This method of modeling might create problems with increasing amounts information, as there is no foolproof way to keep hierarchies like this, or their descendant hierarchies (one is a process, the other is a quality) from entangling or knotting.
In a formal ontology, there is an optimal way to properly classify this concept, it is a kind of 'mobility', which is a kind of quality/property (see above). As a quality, it is said to inhere in independent endurant entities (see above), as such, it cannot exist without a bearer (in the case the arm).
Applications for Formal Ontologies
Formal Ontology as a template to create novel specific domain ontologies
Having a formal ontology at your disposal, especially when it consists of a Formal upper layer enriched with concrete domain-independent 'middle layer' concepts, can really aid the creation of a domain specific ontology.
It allows the modeller to focus on the content of the domain specific ontology without having to worry on the exact higher structure or abstract philosophical
framework that gives his ontology a rigid backbone. Disjoint Axioms
at the higher level will prevent many of the commonly made ontological mistakes made when creating the detailed layer of the ontology.
Formal Ontology as a crossmapping hub: Crossmapping taxonomies, databases and non-Formal ontologies
Aligning terminologies and ontologies is not an easy task. The divergence of the underlying meaning of word descriptions and terms within different information sources is a well known obstacle for direct approaches to data integration and mapping. One single description may have a completely different meaning in one data source when compared with another. This is because different databases/terminologies often have a different viewpoint on similar items. They are usually built with a specific application-perspective in mind and their hierarchical structure represents this.
A formal ontology, on the other hand, represents entities without a particular application scope. Its hierarchy reflects ontological principles and a basic class-subclass relation between its concepts. A consistent framework like this is ideal for crossmapping data sources.
However, one cannot just integrate these external data sources in the formal ontology. A direct incorporation would lead to corruption of the framework and principles of the formal ontology.
A formal ontology is a great crossmapping hub only if a complete distinction between the content and structure of the external information sources and the formal ontology itself is maintained. This is possible by specifying a mapping relation between concepts from a chaotic external information source and a concept in the formal ontology that corresponds with the meaning of the former concept.
Where two or more external information sources map to one and the same formal ontology concept a crossmapping/translation is achieved, as you know that those concepts - no matter what their phrasing is - mean the same thing.
Formal Ontology to empower Natural Language Processing
In ontologies designed to serve Natural language processing
(NLP) and Natural language understanding
(NLU) systems ontology concepts are usually connected and symbolized by terms. This connection represents a linguistic realization.
are words or a combination of words (multi-word units), in different languages, used to describe in natural language an element from reality, and hence connected to that formal ontology concept that frames this element in reality.
, the collection of terms and their inflections assigned to the concepts and relationships in an ontology, forms the ‘ontology interface to natural language’, the channel through which the ontology can be accessed from a natural language input.
Formal Ontology to normalize Database/Instance data
The great thing about a formal ontology, in contrast to rigid taxonomies
, is that it allows for indefinite expansion. Given proper modeling, just about any kind of conceptual
information, no matter the content, can find its place.