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Author Horrocks, Ian ♦ Patel-Schneider, Peter F. ♦ Harmelen, Frank Van
Source CiteSeerX
Content type Text
Publisher AAAI Press
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Daml Oil ♦ Private Off-line Agreement ♦ Language Definition ♦ Computer Software ♦ Design Decision ♦ Familiar Ontological Primitive ♦ Semantic Web Vision ♦ Key Ingredient ♦ Description Logic ♦ Formal Rigor ♦ Web Standard ♦ Current Syntactic Web ♦ Semantic Definition ♦ Syntactic Construct ♦ Semantic Web ♦ Widespread Use ♦ Current Language ♦ Representation Language ♦ Expressive Description Logic ♦ Ontology Language ♦ Implementation Challenge
Description In the current "Syntactic Web", uninterpreted syntactic constructs are given meaning only by private off-line agreements that are inaccessible to computers. In the Semantic Web vision, this is replaced by a web where both data and its semantic definition are accessible and manipulable by computer software. DAML+OIL is an ontology language specifically designed for this use in the Web; it exploits existing Web standards (XML and RDF), adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The definition of DAML+OIL is now over a year old, and the language has been in fairly widespread use. In this paper, we review DAML+OIL's relation with its key ingredients (XML, RDF, OIL, DAML-ONT, Description Logics), we discuss the design decisions and trade-offs that were the basis for the language definition, and identify a number of implementation challenges posed by the current language. These issues are important for designers of other representation languages for the Semantic Web, be they competitors or successors of DAML+OIL, such as the language currently under definition by W3C.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2002-01-01
Publisher Institution IN PROC. OF THE 18TH NAT. CONF. ON ARTIFICIAL INTELLIGENCE (AAAI 2002