The current web suffers information overloading: it is increasingly difficult and time consuming to obtain information desired. Ontologies, the key concept behind the Semantic Web, will provide the means to overcome such problem by providing meaning to the available data. An ontology provides a shared and common understanding of a domain and information machine-processable semantics. To make the Semantic Web a reality and lift current Web to its full potential, powerful and expressive languages are required. Such web ontology languages must be able to describe and organize knowledge in the Web in a machine understandable way. However, organizing knowledge requires the facilities of a logical formalism which can deal with temporal, spatial, epistemic, and inferential aspects of knowledge. Implementations of Web ontology languages must provide these inference services, making them much more than just simple data storage and retrieval systems. This paper presents a state of the art for the most relevant Semantic Web Languages: XML, RDF(s), OIL, DAML+OIL, and OWL, together with a detailed comparison based on modeling primitives and language to language characteristics.
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