Abstract. The visionary approach of Triple Space Computing was recently introduced based on the insight that Web Services do not follow the Web paradigm of ‘persistently publish and read ’ [Fensel, 2004]. Instead, Web Services currently require a synchronous connection to transmit data transparently bypassing and ignoring the power of the Web paradigm. Triple Space Computing proposes to publish communication data analogous to the publication of Web pages: persistently for anybody to read who has access to it at any point in time. This has several benefits. The provider of data can publish it at any point in time (time autonomy), independent of its internal storage (location autonomy), independent of the knowledge about potential readers (reference autonomy) and independent of its internal data schema (schema autonomy). This article introduces a minimal Internet-scalable Triple Space Computing architecture based on Semantic Web technology that implements these four types of autonomy in the simplest way possible with as minimal functionality as feasible to be useful with no or almost no impact to publishers and reader of communication

Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach.

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Abstract. In this paper we present a MOF compliant metamodel and UML profile for the Semantic Web Rule Language (SWRL) that integrates with our previous work on a metamodel and UML profile for OWL DL. Based on this metamodel and profile, UML tools can be used for visual modeling of rule-extended ontologies. 1

In the context of current efforts around Semantic-Web languages, the combination of classical theories in classical first-order logic (and in particular of ontologies in various description logics) with rule languages rooted in logic programming is receiving considerable attention. Existing approaches such as SWRL, dl-programs, and DL+log, differ significantly in the way ontologies interact with (nonmonotonic) rules bases. In this paper, we identify fundamental representational issues which need to be addressed by such combinations and formulate a number of formal principles which help to characterize and classify existing and possible future approaches to the combination of rules and classical theories. We use the formal principles to explicate the underlying assumptions of current approaches. Finally, we propose a number of settings, based on our analysis of the representational issues and the fundamental principles underlying current approaches.

A key challenge for dynamic Web service selection is that Web services are typically highly configurable and service requesters often have dynamic preferences on service configurations. Current approaches, such as WS-Agreement, describe Web services by enumerating the various possible service configurations, an inefficient approach when dealing with numerous service attributes with large value spaces. We model Web service configurations and associated prices and preferences more compactly using utility function policies, which also allows us to draw from multi-attribute decision theory methods to develop an algorithm for optimal service selection. In this paper, we present an OWL ontology for the specification of configurable Web service offers and requests, and a flexible and extensible framework for optimal service selection that combines declarative logic-based matching rules with optimization methods, such as linear programming. Assuming additive price/preference functions, experimental results indicate that our algorithm introduces an overhead of only around 2 sec. compared to random service selection, while giving optimal results. The overhead, as percentage of total time, decreases as the number of offers and configurations increase.

This paper outlines the research we are doing in acquiring, describing and using learning object metadata. Instead of the IEEE LOM and other standardised metadata schemes, we argue for a more flexible approach to both defining and associating metadata with learning objects. This approach, which we call the ecological approach, sees metadata as the process of reasoning over observed interactions of users with a learning object for a particular purpose. Central to this approach is the notion that Semantic Web enabled computational agents will both provide and consume pieces of actual usage data that have been collected about a learning object in determining the usefulness of this learning object for some new purpose. This is then an evolutionary approach to metadata creation as compared to move traditional prescriptive `one size fits all' approaches.

Abstract — The eXtended Markup Language (XML) has emerged in the Internet world as a standard representation format, which can be useful to describe and transmit management information. However, XML formats alone do not give formal semantics to it. To solve this question, ontology languages based on the Resource Description Framework can be used to improve the expressiveness of the management information specifications. This paper presents an approach that uses an XML-based ontology language to define network and system management information. For this, the structures of the Web Ontology Language known as OWL are analyzed and compared to those used in management definitions, studying also the advantages that ontology languages can provide in this area.

Abstract. Description Logics (DLs) are a family of class based knowledge representation formalisms characterised by the use of various constructors to build complex classes from simpler ones, and by an emphasis on the provision of sound, complete and (empirically) tractable reasoning services. They have a range of applications, but are mostly widely known as the basis for ontology languages such as OWL. The increasing use of DL based ontologies in areas such as e-Science and the Semantic Web is, however, already stretching the capabilities of existing DL systems, and brings with it a range of challenges for future research. 1

Abstract. In the recent years rule-based programming in terms of declarative logic programming has formed the basis for many Artificial Intelligence (AI) applications and is well integrated in the mainstream information technology capturing higher-level decision logics. Typically, the standard rule systems and rule-based logic programming languages such as Prolog derivatives are based on the untyped theory of predicate calculus with untyped logical objects (untyped terms), i.e. the logical reasoning algorithms apply pure syntactical reasoning. From a rule engineering perspective this is a serious restriction which lacks major Software Engineering principles such as data abstraction or modularization, which become more and more important when rule applications grow larger and more complex. To support such principles in logic programming and capture the rule engineer’s intended meaning of a logic program, types and typed objects play an important role. Moreover, from a computational point of view, the use of types drastically reduces the search space, i.e. proofs can be kept at a more abstract level and it offers the option to restrict the application of rules and to