By Grigoris Antoniou, Frank van van Harmelen

The advance of the Semantic net, with machine-readable content material, has the capability to revolutionize the area extensive internet and its use. A Semantic net Primer presents an advent and advisor to this rising box, describing its key principles, languages, and applied sciences. appropriate to be used as a textbook or for self-study by way of pros, it concentrates on undergraduate-level basic thoughts and strategies that may let readers to continue with development functions all alone. It comprises routines, undertaking descriptions, and annotated references to proper on-line fabrics. A Semantic internet Primer is the single on hand ebook at the Semantic internet to incorporate a scientific therapy of the several languages (XML, RDF, OWL, and ideas) and applied sciences (explicit metadata, ontologies, and good judgment and inference) which are vital to Semantic net improvement. The booklet additionally examines such an important similar subject matters as ontology engineering and alertness eventualities. After an introductory bankruptcy, subject matters coated in succeeding chapters comprise XML and similar applied sciences that aid semantic interoperability; RDF and RDF Schema, the traditional information version for machine-processable semantics; and OWL, the W3C-approved typical for an internet ontology language extra vast than RDF Schema; principles, either monotonic and nonmonotonic, within the framework of the Semantic internet; chosen program domain names and the way the Semantic net would receive advantages them; the advance of ontology-based platforms; and present debates on key concerns and predictions for the long run.

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Given that AI has a long history, not always commercially successful, one might worry that, in the worst case, the Semantic Web will repeat AI’s errors: big promises that raise too high expectations, which turn out not to be fulfilled (at least not in the promised time frame). This worry is unjustified. The realization of the Semantic Web vision does not rely on human-level intelligence; in fact, as we have tried to explain, the challenges are approached in a different way. The full problem of AI is a deep scientific one, perhaps comparable to the central problems of physics (explain the physical world) or biology (explain the living world).

And third, automated reasoners can deduce (infer) conclusions from the given knowledge, thus making implicit knowledge explicit. Such reasoners have been studied extensively in AI. Here is an example of an inference. Suppose we know that all professors are faculty members, that all faculty members are staff members, and that Michael is a professor. 3 Semantic Web Technologies 13 prof (X) → f aculty(X) f aculty(X) → staff(X) prof (michael) Then we can deduce the following: f aculty(michael) staff(michael) prof (X) → staff(X) Note that this example involves knowledge typically found in ontologies.

Com/semanticWeb-long>. TLFeBOOK TLFeBOOK Suggested Reading 21 • A. Swartz and J. Hendler. The Semantic Web: A Network of Content for the Digital City. html>. • R. Jasper and A. Tyler. The Role of Semantics and Inference in the Semantic Web: A Commercial Challenge. pdf>. There are several courses on the Semantic Web that have extensive material online: • F. van Harmelen et al. Web-Based Knowledge Representation. html>. • J. Heflin. The Semantic Web. edu/∼heflin/courses/semweb/>. • A. Sheth. Semantic Web.

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