- Individuals: instances or objects (the basic or "ground level" objects).
- Classes: sets, collections, concepts, classes in programming, types of objects, or kinds of things.
- Attributes: aspects, properties, features, characteristics, or parameters that objects and classes can have.
- Relations: ways in which classes and individuals can be related to one another.
- Function terms: complex structures formed from certain relations that can be used in place of an individual term in a statement.
- Restrictions: formally stated descriptions of what must be true in order for some assertion to be accepted as input.
- Rules: statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form.
- Axioms: assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application.
- Events: the changing of attributes or relations.
Sunday, March 13, 2016
What is Ontology?
Ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that exist in a particular domain of information. Ontologies are created to limit complexity and to organize information. Ontologies are considered one of the pillars of the Semantic Web.
The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek "being", "that which is". The meaning within information management is a model for describing information that consists of a set of types, properties, and relationship types. Ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations.
The most common ontology visualization techniques are indented tree and graph.
Common components of ontologies include:
Ontologies are commonly encoded using ontology languages.
A domain ontology (or domain-specific ontology) represents concepts which belong to a certain term. Particular meanings of terms applied to that domain are provided by domain ontology. For example, the word "card" has many different meanings. An ontology about the domain of "poker" would model the "playing card" meaning of the word.
Since domain ontologies represent concepts in very specific and often eclectic ways, they are often incompatible. As systems that rely on domain ontologies expand, the need comes to merge domain ontologies into a more general representation. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.).
An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain sets.
There are several standardized upper ontologies available for use such as Dublin Core, for example.
Hybrid ontology is a combination of upper and domain ontology.
Ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. The most commonly used ontology languages are Web Ontology Language (OWL), Resource Description Framework (RDF), RDF Schema (RDFS), Ontology Inference Layer (OIL).
Ontology editors are applications designed to assist in the creation or manipulation of ontologies. They often express ontologies in one of many ontology languages. Some provide export to other ontology languages.
Among the most relevant criteria for choosing an ontology editor are the degree to which the editor abstracts from the actual ontology representation language used for persistence and the visual navigation possibilities within the knowledge model. Also important features are built-in inference engines and information extraction facilities, and the support of meta-ontologies such as OWL-S, Dublin Core, etc. Another important feature is the ability to import & export foreign knowledge representation languages for ontology matching. Ontologies are developed for a specific purpose and application.
Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is labor-intensive and time consuming process, there is a need to automate the process. Information extraction and text mining methods have been explored to automatically link ontologies to documents.
Galaxy Consulting has 16 years experience working with ontologies. Please contact us for a free consultation.