3.3 KiB
| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Formal ontology | 2/2 | https://en.wikipedia.org/wiki/Formal_ontology | reference | science, encyclopedia | 2026-05-05T03:56:47.552517+00:00 | kb-cron |
=== Formal ontology as a crossmapping hub: crossmapping taxonomies, databases and nonformal 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 kind of connection represents a linguistic realization. Terms 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 lexicon, 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 or classifications, 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. To disambiguate a concept's place in the ontology, often a context model is useful to improve the classification power. The model typically applies rules to surrounding elements of the context to select the most valid classification.
== See also == Mereology Ontology (information science) Upper ontology
== References ==