Ontology related concepts

As understood by Philippe A. MARTIN




(Formal/formalized) Ontology

Definition: a list of formal terms and a list of formal definitions/constraint for these terms (and hence any domain representation/specification except for "beliefs" regarding individual objects).

Definition: (From: the Free On-line Dictionary of Computing)

An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.

For AI systems, what "exists" is that which can be represented. When the knowledge about a domain is represented in a declarative language, the set of objects that can be represented is called the universe of discourse. We can describe the ontology of a program by defining a set of representational terms. Definitions associate the names of entities in the universe of discourse (e.g. classes, relations, functions or other objects) with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well-formed use of these terms. Formally, an ontology is the statement of a logical theory.

A set of agents that share the same ontology will be able to communicate about a domain of discourse without necessarily operating on a globally shared theory. We say that an agent commits to an ontology if its observable actions are consistent with the definitions in the ontology. The idea of ontological commitment is based on the Knowledge-Level perspective.

Subtype:
Output of: Ontology construction

Top-level/upper/generic ontology
Definition: "general or basic concepts and relations necessary for doing knowledge representation (e.g. mathematical notions like sets, sequences or numbers) and modelling the physical world (e.g. the animate and inanimate objects, time and space) or problem-solving methods (e.g. for diagnostic or design tasks)."
Subtype:

Broad coverage ontology
Definition: "large ontology not specialised in a particular domain. Except the CYC ontology, the broad coverage ontologies are large sets of concept types related by subsumption relations and sometimes other kinds of relations such as Part-of or Entailment relations."
Example:

Specialised/application ontology
Example:






Ontology construction
Subtask: (Adapted from: the list of current and expected feature of the Ontosaurus Ontology Server)
Subtask of: Knowledge acquisition/engineering (KA/KE)

Composing ontologies (Adapted from: the list of current and expected feature of the Ontosaurus Ontology Server)
Goal: guide and speed the task of ontology construction
Method:
Tools: Loom, etc.

Pruning ontologies (Adapted from: the list of current and expected feature of the Ontosaurus Ontology Server)
Goal: helps a designer to delete terms that are not needed for a given domain

Subtask:

Ontology extraction
Goal: speed the enumeration and organization of the hundreds or thousands terms needed to model a domain
Example:

Ontology merging (Adapted from: the list of current and expected feature of the Ontosaurus Ontology Server)
Goal: helps a designer to augment an ontology or a knowledge base by extracting and incorporating selected parts from independently developed ontologies (cf. figure below).
Example:
Problems:

Ontology validation
Subtask: (Adapted from: the list of current and expected feature of the Ontosaurus Ontology Server)