The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Description Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper we discuss how the philosophy and features of OWL can be traced back to these older formalisms, with modifications driven by several other constraints on OWL. Several interesting problems...

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of first-order logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDFSchema fragment of Description Logic.

Both OWL-DL and function-free Horn rules are decidable fragments of first-order logic with interesting, yet orthogonal expressive power. A combination of OWL-DL and rules is desirable for the Semantic Web; however, it might easily lead to the undecidability of interesting reasoning problems. Here, we present a decidable such combination where rules are required to be DL-safe: each variable in the rule is required to occur in a non-DL-atom in the rule body. We discuss the expressive power of such a combination and present an algorithm for query answering in the related logic SHIQ extended with DL-safe rules, based on a reduction to disjunctive programs.

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We describe an extension of the description logic underlying OWL-DL, SHOIN, with a number of expressive means that we believe will make it more useful in practise. Roughly speaking, we extend SHOIN with all expressive means that were suggested to us by ontology developers as useful additions to OWL-DL, and which, additionally, do not affect its decidability. We consider complex role inclusion axioms of the form R ◦ S ˙ ⊑ R or S ◦ R ˙ ⊑ R to express propagation of one property along another one, which have proven useful in medical terminologies. Furthermore, we extend SHOIN with reflexive, symmetric, transitive, and irreflexive roles, disjoint roles, a universal role, and constructs ∃R.Self, allowing, for instance, the definition of concepts such as a “narcist”. Finally, we consider negated role assertions in Aboxes and qualified number restrictions. The resulting logic is called SROIQ. We present a rather elegant tableau-based reasoning algorithm: it combines the use of automata to keep track of universal value restrictions with the techniques developed for SHOIQ. We believe that SROIQ could serve as a logical basis for possible future extensions of OWL-DL.

OWL DL, a new W3C ontology language recommendation, is based on the expressive description logic SHOIN. Although the ontology consistency problem for SHOIN is known to be decidable, up to now there has been no known “practical ” decision procedure, i.e., a goal directed procedure that is likely to perform well with realistic ontology derived problems. We present such a decision procedure (for SHOIQ, a slightly more expressive logic than SHOIN), extending the well known algorithm for SHIQ,

The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry. Description Logics are often named as one of the tools that can support the Semantic Web and thus help to make this vision reality.

With the development of the semantic web activity, ontologies become widely used to represent the conceptualization of a domain. However, none of the existing ontology languages provides a means to capture uncertainty about the concepts, properties and instances in a domain. Probability theory is a natural choice for dealing with uncertainty. Incorporating probability theory into existing ontology languages will provide these languages additional expressive power to quantify the degree of the overlap or inclusion between two concepts, support probabilistic queries such as finding the most probable concept that a given description belongs to, and make more accurate semantic integration possible. One approach to provide such a probabilistic extension to ontology languages is to use Bayesian networks, a widely used graphic model for knowledge representation under uncertainty. In this paper, we present our on-going research on extending OWL, an ontology language recently proposed by W3C’s Semantic Web Activity. First, the language is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into a Bayesian network. Our probabilistic extension to OWL has clear semantics: the Bayesian network obtained will be associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C | e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most probable concept that a given description belongs to. 1.

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies. We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties. The system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments carried out show the good correspondence between users and system rankings.

Ontologies are set to play a key role in the Semantic Web, and several web ontology languages, like DAML+OIL, are based on DLs.