Where Oi: ontology, eOi,    fOj: entities of (Oi, Oj) Sim (eO1, fO2): similarity function between two entities eO1 and fO2. The ontology alignment function is based on different similarity measures. A similarity measure is a real‐valued function Sim (ei, fj): O×O  [0, 1]
measuring the degree of similarity between x and y.

## 4.4.6 **Other Ontology Operations**

There are many operations that could apply to ontology, such as changing [23], which is considered one of the most interesting and important operations that should be taken into account when dealing with ontology. 

In general, most existing ontologies have large sizes and complex structures. In fact, several factors could be responsible for a change in ontology, including a response to new needs or requirements, a change by the developer or the editor of ontology, an ontological translation from one language to another and a change of domain of interest. On the other hand, using versioning could help to reduce those problems by keeping track of the relationships between different revisions of ontology [23]. As argued in [23] ontology versioning is the ability to handle changes in ontologies by creating and managing different ontological variants. 

## 5. Ontology Languages, **Ontology** Editors And Development Tools

The main object of semantic web languages [35] is to add semantics to the existing information on the Web. RDF/RDFS [5], OIL [15], DAML+OIL [18] and OWL [1] are modelling web languages that have been developed to represent or express ontologies. In general, most of them are based on XML [4] syntax, but they have different terminologies and expressions. 

Indeed, some of these languages have the ability to represent certain logical relations which others do not. Because some languages have greater expressive power than others, their selection for representing ontologies is based mainly on what the ontology represents or what it will be used for. In other words, different kinds of ontological knowledge-based applications need different language facilitators to enable reasoning on ontology data. These description languages provide richer constructors for forming complex class expressions and axioms. In fact, most recent ontology developers have used ontology editors, which are environments or tools used directly for editing, developing or modifying ontologies. They are used to provide support to the ontological development process, as well as to conceptualise the ontology; they transform the conceptualisation into an executable code using translators. Therefore, the output ontology of these tools will be in one of the Web ontology languages supported by editors such as Protégé [30], OWL-P [8] and OilEd [3]. Alternatively, ontology reasoners are used to check the conflicts with a high degree of automation. Many such systems have recently been developed, including RACER [17] and FaCT [37]. Returning to the main concern of this section, modelling web languages, there are in general two different types: presentation languages such as HTML, designed to represent text and images to users or requesters without reference to the content, and data languages, intended to be processed by machines. The present research relates to the latter. 

Before OWL, much research had been conducted into creating a powerful ontology modelling language. This research stream began with the XML-based RDF and RDF/S, progressed to the Ontology Inference Layer (OIL) and continued with the creation of DAML+OIL, the result of joining the American proposal DAMLONT5 with the European language OIL. All these languages are based on XML or RDF 