TRIPLE - an RDF Rule Language with Context and Use Cases

Rules and Rule Markup Languages for the …, 2004

In this paper we present R-DEVICE, a deductive rule language for reasoning about RDF metadata. R-DEVICE includes features such as normal and generalized path expressions, stratified negation, aggregate, grouping, and sorting, functions. The rule language supports a second-order syntax, where variables can range over classes and properties. Users can define views which are materialized and, optionally, incrementally maintained by translating deductive rules into CLIPS production rules. Users can choose between an OPS5/CLIPS-like or a RuleML-like syntax. R-DEVICE is based on a OO RDF data model, different than the established graph model, which maps resources to objects and encapsulates properties inside resource objects, as traditional OO attributes. In this way, less joins are required to access the properties of a single resource resulting in better inferencing/querying performance. The descriptive semantics of RDF may call for dynamic re-definitions of resource classes and objects, which are handled by R-DEVICE effectively.

Defeasible reasoning is a rule4based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. In this demonstration we pre4 sent a prototype system for defeasible reasoning on the Web.

2008

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-asfailure 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.

International Journal on Semantic Web …, 2006

In this paper we present R-DEVICE, a deductive object-oriented knowledge base system for reasoning over RDF metadata. R-DEVICE imports RDF documents into the CLIPS production rule system by transforming RDF triples into COOL objects and uses a deductive rule language for reasoning about them. R-DEVICE is based on an OO RDF data model, different than the established triple-based model, which maps resources to objects and encapsulates properties inside resource objects, as traditional OO attributes. In this way, fewer joins are required to access the properties of a single resource resulting in better inferencing/querying performance, as it is experimentally shown in the paper. Furthermore, RDF can interoperate seamlessly with other web data models and languages. The descriptive semantics of RDF may call for dynamic redefinitions of resource classes, which are handled by R-DEVICE effectively. Furthermore, R-DEVICE features a powerful deductive rule language for reasoning on top of RDF metadata. The rule language includes features such as normal and generalized path expressions, stratified negation, aggregate, grouping, and sorting, functions. The rule language supports a second-order syntax, which is efficiently translated into sets of firstorder logic rules using metadata, where variables can range over classes and properties, so that reasoning over the RDF schema can be made. Users can define views which are materialized and incrementally maintained by translating deductive rules into CLIPS production rules that preserve truth. Users can choose between an OPS5/CLIPS-like and a RuleML-like syntax. Finally, users can define and use functions through the CLIPS host language.

International Journal on Semantic Web and Information Systems, 2006

In this paper we present R-DEVICE, a deductive object-oriented knowledge base system for reasoning over RDF metadata. R-DEVICE imports RDF documents into the CLIPS production rule system by transforming RDF triples into COOL objects and uses a deductive rule language for reasoning about them. R-DEVICE is based on an OO RDF data model, different than the established triple-based model, which maps resources to objects and encapsulates properties inside resource objects, as traditional OO attributes. In this way, fewer joins are required to access the properties of a single resource resulting in better inferencing/querying performance, as it is experimentally shown in the paper. Furthermore, RDF can interoperate seamlessly with other web data models and languages. The descriptive semantics of RDF may call for dynamic redefinitions of resource classes, which are handled by R-DEVICE effectively. Furthermore, R-DEVICE features a powerful deductive rule language for reasoning on top of RDF metadata. The rule language includes features such as normal and generalized path expressions, stratified negation, aggregate, grouping, and sorting, functions. The rule language supports a second-order syntax, which is efficiently translated into sets of firstorder logic rules using metadata, where variables can range over classes and properties, so that reasoning over the RDF schema can be made. Users can define views which are materialized and incrementally maintained by translating deductive rules into CLIPS production rules that preserve truth. Users can choose between an OPS5/CLIPS-like and a RuleML-like syntax. Finally, users can define and use functions through the CLIPS host language.