1.2 Ontology representation languages

Another more comprehensive methodology is the Methontology framework (Fernández-López et al. 1999). It starts from the assumption that the entire ontology lifecycle has to be defined and standardized through the following phases:

• Specification – Identification of the ontology’s terminology, primary objective, purpose, granularity level, and scope; • Conceptualization – Organizing and structuring in a semiformal way the knowledge acquired during the specification phase, using a set of intermediate representations that both domain experts and ontologists can understand (thus bridging the gap between their mindsets); • Implementation – Using an ontology development environment to formally represent and implement the products of the above two phases, namely concepts, hierarchies, relations, and models. In addition, Methontology covers processes that run in parallel throughout the ontology life cycle. In particular can be cited: quality assurance, integration, evaluation, maintenance, documentation, and configuration management. Furthermore, it specifies in detail the techniques used in each activity, the products that each activity outputs, and how they have to be evaluated. Also, knowledge acquisition is recognized as a crucial activity in the framework that influences the specification and conceptualization phases and count for the long shoulder-to-shoulder work with domain experts. It comprises the use of various knowledge acquisition techniques to create a preliminary version of the ontology specification, as well as all of the intermediate representations resulting from the conceptualization phase. The basic concepts provided above should have put us in the condition to figure out what an ontology could be, for which purposes could be useful and which could be the first steps in its development. Next paragraphs will complete our overview on ontologies touching base with three fundamental aspects related to their: representation languages; development environments and visualization.

1.2 Ontology representation languages

When we know how to design and build an ontology, there is the need to think about deploying it. But what does it mean? In other words, the ontology itself has to be represented in a way that is suitable for the computer and system applications intended to work with it. This paragraph looks at the various machine-readable standards, from XML to RDFS, leading up to OWL that is the language used for the proposed ontology, presented in Chapter 3.

There are several ontology representation languages. Some of them were developed at the beginning of the 1990s within the AI research branch. Others were presented in the late 1990s and later, resulting from the AI community and the World Wide Web Consortium (W3C). The early ontology representation languages belong to the pre-XML era, whereas the later ones are XML-based. Most of the later ones were developed to support ontology representation on the Semantic Web, and hence they are also called “Semantic Web languages.” Other common names for them are “Web-based ontology languages” and “ontology markup languages”. In the following list some of the best-known examples of ontology representation languages are provided along with specific references to their more detailed description:

• KIF (Genesereth and Fikes 1992), based on first-order logic; • Ontolingua (Gruber 1992), built on top of KIF but including frame-based representation; • Loom (MacGregor 1991), based on description logics. • Among the widely used Web-based ontology languages can be found: • SHOE (Luke and Heflin 2000), built as an extension of HTML; • XOL (Karp et al. 1999), developed by the AI center of SRI International as an XML-ization of a small subset of primitives from the OKBC protocol called OKBC-Lite; • RDF (Manola and Miller 2004), developed by the W3C as a semantic network-based language to describe Web resources; • RDF Schema (Brickley and Guha 2004), also developed by the W3C, is an extension of

RDF with frame-based primitives; the combination of both RDF and RDF Schema is known as RDF(S); • OIL (Fensel et al. 2001), which is based on description logics and includes frame-based representation primitives; • DAML+OIL (Horrocks and van Harmelen 2002) is the latest release of the earlier DAML (DARPA Agent Markup Language), created as the result of a joint effort of DAML and

OIL developers to combine the expressiveness of the two languages; • OWL, or Web Ontology Language (Smith et al. 2004), developed under the auspices of the W3C and evolved from DAML+OIL; OWL is currently the most popular ontology representation language. The reason why during the years a number of ontology representation languages have been developed are clearly expressed in the following sentence by (Decker et. Al 2000): «Ideally, we would like a universal shared knowledge-representation language to support the Semantic Web, but for a variety of pragmatic and technological reasons, this is unachievable in practice. Instead, we will have to live with a multitude of metadata representations».