The word ontology was taken from Philosophy, where it means a systematic explanation of being. In the last decade, this word has become relevant for the Knowledge Engineering community. An ontology defines the basic terms and relations comprising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary. This definition identifies basic terms and relations between terms, identifies rules to combine terms, and provides the definitions of such terms and relations. Note that, an ontology includes not only the terms that are explicitly defined in it, but also the knowledge that can be inferred from it. 

Also ontology can be defined as a formal, explicit specification of a shared conceptualization.

·         Conceptualization refers to an abstract model of some phenomenon in the world by having identified the relevant concepts of that phenomenon.

·         Explicit means that the type of concepts used, and the constraints on their use are explicitly defined.

·         Formal refers to the fact that the ontology should be machine-readable.

·         Shared reflects the notion that  an ontology captures consensual knowledge, that is not private of some individual, but accepted by a group.

Types of Ontologies

There are four types of ontologies: top-level, domain, task, and application ontology.

Top-level Ontologies or Upper-level Ontologies describe very general concepts and provide general notions under which all root terms in existing ontologies should be linked.

Domain ontologies are reusable in a given specific domain (medical, pharmaceutical, engineering, law, enterprise, automobile, etc.). These ontologies provide vocabularies about concepts within a domain and their relationships, about the activities taking place in that domain, and about the theories and elementary principles governing that domain. There is a clean boundary between domain and upper-level ontologies. The concepts in domain ontologies are usually specializations of concepts already defined in top-level ontologies, and the same might occur with the relations. For instance, the term ‘City’ in a domain ontology is a specialization of a more generic concept ‘Location’, which is a specialization of the term ‘SpatialPoint’ that may be defined on the upper-level ontology. Similarly, the relation ‘connects’ defined in an upper-level ontology can be specialized to express that a road connects two cities (roadConnectsCities) in a domain ontology.

Task ontologies describe the vocabulary related to a generic task or activity (like diagnosing, scheduling, selling, etc.) by specializing the terms in the top-level ontologies. Task ontologies provide a systematic vocabulary of the terms used to solve problems associated with tasks that may or may not belong to the same domain.

Application ontologies are application-dependent. They contain all the definitions needed to model the knowledge required for a particular application. Application ontologies often extend and specialize the vocabulary of the domain and of task ontologies for a given application. For instance, we could create an application ontology for Spanish travel agencies specialized in North American destinations.

Applications of ontologies

Ontologies can be used to support a great variety of tasks in diverse research areas such as knowledge representation, natural language processing, information retrieval, databases, knowledge management, on line database integration, digital libraries, geographic information systems, visual information retrieval or multi agent systems.

An ontology provides meta information which describes data semantics. Ontologies enable shared knowledge and reuse where information resources can be communicated between human or software agents. Semantical relationships in ontologies are machine readable; in such a way they enable making statements and asking queries about a subject domain due to the use of a conceptualization, which describes entities and their relationships. This conceptualization enables those software agents of a vocabulary to represent and to communicate knowledge. The usefulness of ontologies in agent based systems can be briefly summarized as they enable knowledge−level interoperation. In other research areas, ontologies support shared understanding, interoperability between tools, systems engineering, reusability and declarative specification.

Ontologies are used to build knowledge bases, a knowledge bases is formed by an ontology and a set of individual instances of its classes. Knowledge bases can be queried by agents in order to enrich, reuse and maintain them. Ontologies concentrate state−independent information while the core of knowledge bases is formed by state−dependent information.

Ontologies are able to operate as repositories to organize information for specific communities. They can be used as a tool for knowledge acquisition, (team works can use ontologies as a common support to classify the knowledge of an organization). Ontologies allow users to reuse knowledge in new systems. They can form a base to construct knowledge representation languages.

Semantic integration of heterogeneous information sources such as digital libraries can benefit with the incorporation of ontologies. Some applications use a domain ontology to integrate information resources and others allow each resource to use its own ontology.  Each user can also have his own ontology according to his/her interests, language or role in a determine domain. Ontologies provide a source of precisely defined terms.

In information retrieval applications, ontologies serve to disambiguate user queries, to elaborate taxonomies of terms or thesaurus in order to enhance the quality of retrieved results. Machine learning techniques are also used to extend ontologies based on user’s interactions.