Stephan Grimm

Preference-based selection of highly configurable web services

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.

Automated business-to-business integration of a logistics supply chain using semantic web services technology

In this paper, we present a demonstrator system which applies semantic web services technology to business-to-business integration, focussing specifically on a logistics supply chain. The system is able to handle all stages of the service lifecycle - discovery, service selection and service execution. One unique feature of the system is its approach to protocol mediation, allowing a service requestor to dynamically modify the way it communicates with aprovider, based on a description of the providers protocol. We present the architecture of the system, together with an overview of the key components (discovery and mediation) and the implementation.

Knowledge Representation and Ontologies

Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for com–putational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.

Matching semantic service descriptions with local closed-world reasoning

Semantic Web Services were developed with the goal of automating the integration of business processes on the Web. The main idea is to express the functionality of the services explicitly, using semantic annotations. Such annotations can, for example, be used for service discovery—the task of locating a service capable of fulfilling a business request. In this paper, we present a framework for annotating Web Services using description logics (DLs), a family of knowledge representation formalisms widely used in the Semantic Web.We show how to realise service discovery by matching semantic service descriptions, applying DL inferencing. Building on our previous work, we identify problems that occur in the matchmaking process due to the open-world assumption when handling incomplete service descriptions. We propose to use autoepistemic extensions to DLs (ADLs) to overcome these problems. ADLs allow for non-monotonic reasoning and for querying DL knowledge bases under local closed-world assumption. We investigate the use of epistemic operators of ADLs in service descriptions, and show how they affect DL inferences in the context of semantic matchmaking.

Semantic Matchmaking of Web Resources with Local Closed-World Reasoning

Ontology languages like OWL allow for semantically rich annotation of resources (e.g., products advertised at on-line electronic marketplaces). The description logic (DL) formalism underlying OWL provides reasoning techniques that perform match-making on such annotations. This paper identifies peculiarities in the use of DL inferences for matchmaking that derive from OWLs open-world semantics, analyzes local closed-world reasoning for its applicability to matchmaking, and investigates the suitability of two nonmonotonic extensions to DL, autoepistemic DLs and DLs with circumscription, for local closed-world reasoning in the matchmaking context. An elaborate example of an electronic marketplace for PC product catalogs from the e-commerce domain demonstrates how these formalisms can be used to realize such scenarios.

Supporting business intelligence by providing ontology-based end-user information self-service

Business users need to analyse changing sets of information to effectively support their working tasks. Due to the complexity of enterprise systems and available tools, especially technically unskilled users face considerable challenges when trying to flexibly retrieve needed data in an ad-hoc manner. As a consequence, available data is limited to information artefacts like queries or reports which have been predefined for them by IT experts. To improve information self-service capabilities of business users, we present an ontology-based architecture and end-user tool, enabling easy data access and query creation for business users. Our approach is based on a semantic middleware integrating data from heterogeneous information systems and providing a comprehensible data model in the form of a business level ontology (BO). We show how our end-user tool Semantic Query Designer (SQD) enables convenient navigation and query building upon the BO, and illustrate its usage and the processing of data over all layers of our system architecture in detail, using a comprehensible use case example. As flexible query creation is a crucial precondition of leveraging the usage of enterprise data, we contribute to the enablement of business users of making better informed decisions, thus increasing effectiveness and efficiency of business processes.

Feature ontologies for the explicit representation of shape semantics

CAx systems typically encode the semantics of shapes as so-called parametric features on different levels of abstraction. Here we discuss an approach that combines feature-based parametric modelling with techniques from the field of knowledge representation and ontological reasoning. Parametric models refer to feature ontologies that model feature semantics on several levels of granularity. On higher levels, the interrelation between features and feature interoperability is captured whereas on lower levels a feature is described in terms of geometric, topological and parametric entities. Different engineering tasks can utilise feature ontologies as a basis for application-specific shape reasoning across several modelling layers.

Elimination of redundancy in ontologies

Ontologies may contain redundancy in terms of axioms that logically follow from other axioms and that could be removed for the sake of consolidation and conciseness without changing the overall meaning. In this paper, we investigate methods for removing such redundancy from ontologies. We define notions around redundancy and discuss typical cases of redundancy and their relation to ontology engineering and evolution. We provide methods to compute irredundant ontologies both indirectly by calculating justifications, and directly by utilising a hitting set tree algorithm and module extraction techniques for optimization. Moreover, we report on experimental results on removing redundancy from existing ontologies available on the Web.

A Preferential Tableaux Calculus for Circumscriptive

Nonmonotonic extensions of description logics (DLs) allow for default and local closed-world reasoning and are an acknowledged desired feature for applications, e.g. in the Semantic Web. A recent approach to such an extension is based on McCarthys circumscription, which rests on the principle of minimising the extension of selected predicates to close off dedicated parts of a domain model. While decidability and complexity results have been established in the literature, no practical algorithmisation for circumscriptive DLs has been proposed so far. In this paper, we present a tableaux calculus that can be used as a decision procedure for concept satisfiability with respect to concept-circumscribed

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