RDF and Description Logics work in an open-world setting where absence of information is not information about absence. Nevertheless, Description Logic axioms can be interpreted in a closed-world setting and in this setting they can be used for both constraint checking and closed-world recognition against information sources. When the information sources are expressed in well-behaved RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this constraint checking and closed-world recognition is simple to describe. Further this constraint checking can be implemented as SPARQL querying and thus effectively performed.

In this paper, we present a speech-driven second screen application for TV program discovery. We give an overview of the application and its architecture. We also present a user study along with a failure analysis. The results from the study are encouraging, and demonstrate our application’s effectiveness in the target domain. We conclude with a discussion of follow-on efforts to further enhance our application.

Semantic technologies are increasingly being employed to integrate, relate and classify heterogeneous data from various problem domains. To date, however, little empirical analysis has been carried out to help identify the benefits and limitations of different semantic approaches on specific data integration and classification problems. This paper evaluates three alternative semantic techniques for performing classification over data derived from the telecommunications domain. The problem of interest involves inferring the "health" status of network nodes (femtocells) from synthesized performance management (PM) instance data based on the operational PM schema. The semantic approaches used in the comparison include OWL2 axioms, SPARQL queries and SWRL rules. Empirical tests were performed across a range of data set sizes, using Pellet for axioms and rules and ARQ for queries. The experimental results provide (mostly) quantitative and (some) qualitative indication of the relative merits of each approach. Key among these findings is confirmation of the clear superiority of queries over rules and axioms in terms of raw performance and scalability.

Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automatically translated or inferred using the axioms of RDFS/OWL, via user generated rules, or as the result of SPARQL query result transformations. For a given problem it is not always obvious which approach (or combination of approaches) will be the most effective and few guidelines exist for making this choice. This paper discusses these three approaches and demonstrates them using an "acquaintance" relationship drawn from data residing in common RDF information sources such as FOAF and DBLP data stores. The implementation of each approach is described along with practical considerations for their use. Quantitative and qualitative evaluation results of each approach are presented and the paper concludes with initial suggestions for guiding principles to help in selecting an appropriate approach for integrating heterogeneous semantic data sources.

Classical logics and Datalog-related logics have both been proposed as underlying formalisms for the Semantic Web. Although these two different formalism groups have some commonalities, and look similar in the context of expressively-impoverished languages like RDF, their differences become apparent at more expressive language levels. After considering some of these differences, we argue that, although some of the characteristics of Datalog have their utility, the open environment of the Semantic Web is better served by standard logics.

The web and converged services paradigm promises tremendous flexibility in the creation of rich composite services for enterprises and end users. The flexibility and richness offers the possibility of highly customized, individualized services for the end user and hence revenue generating services for service providers (e.g. ASPs, telecom network operators, ISPs). But how can end users (and enterprises) specify their preferences when a myriad of possibilities and potential circumstances need to be addressed? In this paper, we advocate a solution based on policy management where user preferences are specified through forms but translated into rules in a high-level policy language. This paper identifies the requirements for this kind of interpretation, and describes the Houdini system (developed at Bell Labs) which offers a rich rule-based language and a framework that supports intuitive, forms-based provisioning interfaces.

This paper provides detail on two key components of the Houdini framework under development at Bell Labs, that enable context-aware and privacy-conscious user data sharing appropriate for mobile and/or ubiquitous computing. The framework includes an approach for integrating data from diverse sources, for gathering user preferences for what data to share and when to share it, and a policy management infrastructure in the network for enforcing those preferences. The current paper focuses on two components of this infrastructure that are essential for mobile and ubiquitous computing, namely the framework to support self-provisioning of preferences, and the performance of the underlying rules engine.

Although the OWLWeb Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. Wepresent ORL (OWL Rules Language), a Horn clause rules extension to OWL that overcomes many of these limitations. ORL extends OWL in a syntactically and semantically coherent manner: the basic syntax for ORL rules is an extension of the abstract syntax for OWL DL and OWLLite; ORL rules are given formal meaning via an extension of the OWLDL model-theoretic semantics; ORL rules are given an XML syntax basedon the OWL XML presentation syntax; and a mapping from ORL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. Wediscuss the expressive power of ORL, showing that the ontology consistency problem is undecidable, provide several examples of ORLusage, and discuss how reasoning support for ORL might be provided.

The Semantic Web is vitally dependent on a formal meaning for the constructs of its languages. For Semantic Web languages to work well together their formal meanings must employ a common view (or thesis) of representation, otherwise it will not be possible to reconcile documents written in different languages. The thesis of representation underlying RDF and RDFS is particularly troublesome in this regard, as it has several unusual aspects, both semantic and syntactic. A more-standard thesis of representation would result in the ability to reuse existing results and tools in the Semantic Web.

The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests.

XML is the W3C standard document format for writing and exchanging information on the Web. RDF is the W3C standard model for describing the semantics and reasoning about information on the Web. Unfortunately, RDF and XML-although very close to each other-are based on two different paradigms. We argue that, in order to lead the Semantic Web to its full potential, the syntax and the semantics of information need to work together. To this end, we develop a model theory for the XML XQuery 1.0 and XPath 2.0 Data Model, which provides a unified model for both XML and RDF. This unified model can serve as the basis for Web applications that deal with both data and semantics. We illustrate the use of this model on a concrete information integration scenario. Our approach enables each side of the fence to benefit from the other, notably, we show how the RDF world can take advantage of XML Schema description and XML query languages, and how the XML world can take advantage of the reasoning capabilities available for RDF. Our approach can also serve as a foundation for the next layer of the Semantic Web, the ontology layer, and we present a layering of an ontology language on top of our approach.

XML is the W3C standard document format for writing and exchanging information on the Web. RDF is the W3C standard model for describing the semantics and reasoning about information on the Web. Unfortunately, RDF and XML---although very close to each other---are based on two different paradigms. We argue that in order to lead the Semantic Web to its full potential, the syntax and the semantics of information needs to work together. To this end, we develop a model-theoretic semantics for the XML XQuery 1.0 and XPath 2.0 Data Model, which provides a unified model for both XML and RDF. This unified model can serve as the basis for Web applications that deal with both data and semantics. We illustrate the use of this model on a concrete information integration scenario. Our approach enables each side of the fence to benefit from the other, notably, we show how the RDF world can take advantage of XML query languages, and how the XML world can take advantage of the reasoning capabilities available for RDF.

Object-oriented languages and rule-based languages offer two distinct and useful programming abstractions. However, previous attempts to integrate data-driven rules into object-oriented languages have typically achieved an uneasy union at best. R++ is a new, closer integration of the rule-based and object-oriented paradigms that extends C++ with a single programming construct, the path-based rule, as a new kind of class member. Path-based rules-data-driven rules that are restricted to following pointers between objects-are like automatic methods that are triggered by changes to the objects they monitor. Path-based rules provide a useful level of abstraction that encourages a more declarative style of programming and are valuable in object-oriented designs as a means of modeling dynamic collections of interdependent objects. Unlike more traditional pattern-matching rules, path-based rules are not at odds with the object-oriented paradigm and offer performance advantages for many natural applications.

E Franconi, G De Giacomo, IR Horrocks, DL McGuinness, W Nutt, PF Patel-Schneider, CA Welty; Conferences. Report on the 1998 International Workshop on Descriptio

Effective optimization techniques can make a dramatic difference in the performance of knowledge representation systems based on expressive description logics. With currently-available desktop computers, systems that incorporate these techniques can effectively reason in description logics with intractable inference. Because of the correspondence between description logics and propositional modal logic difficult problems in a propositional modal logic can be effectively solved using the same techniques. Key words: Description logic systems, optimization, propositional modal logics

ABell Labs Research Murray Hill, NJ, USA LRI, University of Paris-Sud, France

Standard object-oriented languages do not provide language support for modeling changing collections of interdependent objects. We propose that R++, an integration of the rule and objectoriented paradigms, provides a mechanism for easily implementing such models. R++ extends C++ by adding a new programming construct called the path-based rule. Such data-driven rules are restricted to follow pointers between objects, and are like "automatic methods" that are triggered by changes to monitored objects. Path-based rules encourage a more abstract level of programming, and unlike previous rule integrations, are not at odds with the object-oriented paradigm and offer performance advantages for natural applications. 1 Introduction Object-oriented languages have simplified the design and implementation of sophisticated applications. However, as application domains have become increasingly dynamic and complex, it has become necessary to model such domains using changing collections of interdep...

This paper analyzes the correctness of the subsumption algorithm used in CLASSIC, a description logic-based knowledge representation system that is being used in practical applications. In order to deal efficiently with individuals in CLASSIC descriptions, the developers have had to use an algorithm that is incomplete with respect to the standard, model-theoretic semantics for description logics. We provide a variant semantics for descriptions with respect to which the current implementation is complete, and which can be independently motivated. The soundness and completeness of the polynomial-time subsumption algorithm is established using description graphs, which are an abstracted version of the implementation structures used in CLASSIC, and are of independent interest.

The major problem with using standard first‐order logic as a basis for knowledge representation systems is its undecidability. A variant of first‐order tautological entailment, a simple version of relevance logic, has been developed that has decidable inference and thus overcomes this problem. However, this logic is too weak for knowledge representation and must be strengthened. One way to strengthen the logic is to create a hybrid logic by adding a terminological reasoner. This must be done with care to retain the decidability of the logic as well as its reasonable semantics. The result, a stronger decidable logic, is used in the design of a hybrid, decidable, logic‐based knowledge representation system.