RDFS and OWL are languages with clearly defined semantics or mathematical basis for the meaning of each construct. Semantics are defined in terms of inferences each statement entails. Since concepts in RDFS and OWL ontologies are expressed formally, they can be processed by computer programs. Because traditional modeling techniques (such as entity relationship) describe concepts only semi-formally, they cannot be handled automatically by software without significant human programming effort to make their meanings explicit.

Similarities and differences between ontologies and other types of information models can be described in the following way:

Like databases, ontologies are used by applications at run time (queried and reasoned over) --Unlike databases, relationships are first-class constructs --Unlike databases, ontologies can easily change their schema at run-time

Like object models, ontologies describe classes and attributes (properties) --Unlike object models, ontologies are set-based

Like business rules, ontologies encode rules --Unlike business rules, ontologies organize rules using axioms

Like XML schemas, ontologies are native to the web (and can be serialized in XML) --Unlike XML schemas, ontologies are graphs not trees and can be used for reasoning

An ontology defines the concepts and relationships used to describe and represent an area of knowledge (subject matter). It provides:

• A vocabulary
• A shared language

An ontology is defined using constructs for:

Concepts: classes Relationships: properties (object and data) Rules: axioms and constraints Instances of concepts: individuals (data)

Ontologies can be used for many purposes, including enterprise integration, information retrieval, network management and information interchange on the World Wide Web. Ontologies can contain business rules and definitions which are processed and enforced at run time by standard inferencing components. This enables application code for semantic (ontology-based) systems to be thinner and more generic.