Computer Science – Artificial Intelligence
Scientific paper
2011-10-31
Journal Of Artificial Intelligence Research, Volume 32, pages 37-94, 2008
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.2425
Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.
Analyti Anastasia
Antoniou Grigoris
Damásio Carlos Viegas
Wagner Gerd
No associations
LandOfFree
Extended RDF as a Semantic Foundation of Rule Markup Languages does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Extended RDF as a Semantic Foundation of Rule Markup Languages, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Extended RDF as a Semantic Foundation of Rule Markup Languages will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-466757