Alessio Gugliotta

IRS-III: A broker-based approach to semantic Web services

A factor limiting the take up of Web services is that all tasks associated with the creation of an application, for example, finding, composing, and resolving mismatches between Web services have to be carried out by a software developer. Semantic Web services is a combination of semantic Web and Web service technologies that promise to alleviate these problems. In this paper we describe IRS-III, a framework for creating and executing semantic Web services, which takes a semantic broker-based approach to mediating between service requesters and service providers. We describe the overall approach and the components of IRS-III from an ontological and architectural viewpoint. We then illustrate our approach through an application in the eGovernment domain.

IRS-III: a broker for semantic web services based applications

In this paper we describe IRS-III which takes a semantic broker based approach to creating applications from Semantic Web Services by mediating between a service requester and one or more service providers. Business organisations can view Semantic Web Services as the basic mechanism for integrating data and processes across applications on the Web. This paper extends previous publications on IRS by providing an overall description of our framework from the point of view of application development. More specifically, we describe the IRS-III methodology for building applications using Semantic Web Services and illustrate our approach through a use case on e-government.

Exploiting Metrics for Similarity-Based Semantic Web Service Discovery

Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. However, heterogeneities between distinct SWS representations pose strong limitations w.r.t. interoperability and reusability. Hence, semantic level mediation, i.e. mediation between concurrent semantic representations, is a key requirement to allow SWS matchmaking algorithms to compare capabilities of distinct SWS. In that, semantic level mediation requires to identify similarities across distinct SWS representations. Since current approaches to mediate between distinct service annotations rely either on manual one-to-one mappings or on semi-automatic mappings based on the exploitation of linguistic or structural similarities, these are perceived to be costly and error-prone. We propose a mediation approach enabling the implicit representation of similarities across distinct SWS by grounding these in so-called Mediation Spaces (MS). Given a set of SWS and their respective MS grounding, a general-purpose mediator automatically computes similarities to identify the most appropriate SWS for a given request. A prototypical application illustrates our approach.

Deploying semantic web services-based applications in the e-government domain

Joining up services in e-Government usually implies governmental agencies acting in concert without a central control regime. This requires to the sharing scattered and heterogeneous data. Semantic Web Service (SWS) technology can help to integrate, mediate and reason between these datasets. However, since a few real-world applications have been developed, it is still unclear which are the actual benefits and issues of adopting such a technology in the e-Government domain. In this paper, we contribute to raising awareness of the potential benefits in the e-Government community by analyzing motivations, requirements and expected results, before proposing a reusable SWS-based framework. We demonstrate the application of this framework by showing how integration and interoperability emerge from this model through a cooperative and multi-viewpoint methodology. Finally, we illustrate added values and lessons learned by two compelling case studies: a change of circumstances notification system and a GIS-based emergency planning system, and describe key challenges which remain to be addressed.

Towards context-aware semantic web service discovery through conceptual situation spaces

Context-awareness is highly desired across several application domains. Semantic Web Services (SWS) enable the automatic discovery of distributed Web services based on comprehensive semantic representations. However, although SWS technology supports the automatic allocation of resources for a given well-defined task, it does not entail the discovery of appropriate SWS representations for a given situational context. Whereas tasks are highly dependent on the situational context in which they occur, SWS technology does not explicitly encourage the representation of domain situations. Moreover, describing the complex notion of a specific situation in all its facets is a costly task and may never reach semantic completeness. Particularly, following the symbolic SWS approach leads to ambiguity issues and does not entail semantic meaningfulness. Apart from that, not any real-world situation completely equals another, but has to be matched to a finite set of semantically defined parameter descriptions to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) which are aligned to established SWS standards. CSS enable the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. Semantic similarity between situations is calculated in terms of their Euclidean distance within a CSS. Extending merely symbolic SWS descriptions with context information on a conceptual level through CSS enables similarity-based matchmaking between real-world situation characteristics and predefined resource representations as part of SWS descriptions. To prove the feasibility, we apply our approach to the domain of E-Learning and provide a proof-of-concept prototype.

A Semantic Web Service Oriented Framework for Adaptive Learning Environments

The current state of the art in supporting e-learning objectives is primarily based on providing a learner with learning content by using metadata standards. Due to this approach, several issues have to be taken into account --- e. g. limited re-usability across different standards and learning contexts and high development costs. To overcome these issues, this paper describes an innovative semantic web service-oriented framework aimed at changing this data- and metadata-based paradigm to a highly dynamic service-oriented approach. Instead of providing a learner with static data, our approach is based on fulfilling learning objectives based on a dynamic supply of services. Therefore, we introduce a semantic layer architecture to abstract from existing learning data as well as process metadata standards by using Semantic Web Service (SWS) technology. Furthermore, our approach is based on abstract and reusable learning process models describing a learning process semantically as a composition of learning goals. Based on the formal semantic descriptions of learning goals as well as web services, services appropriate to achieve a specific learning goal can be selected, composed and invoked dynamically. This supports a high level of re-usability since a dynamic adaptation to different learning contexts and requirements of individual learners is achieved while utilizing standard-compliant learning applications. To illustrate the application of our approach, we describe a prototypical implementation utilizing the introduced approach based on the SWS framework WSMO.

COMPOSE -- A Journey from the Internet of Things to the Internet of Services

The COMPOSE project aims to unleash the full potential harbored by the Internet of Things by creating a complete ecosystem around it to enable the flourishing of a resulting Internet of Services, seamlessly integrating the real and virtual worlds. COMPOSE will achieve this through the provisioning of an open and scalable marketplace infrastructure, in which smart objects are associated to services that can be combined, managed, and integrated in a standardized way to easily build innovative applications. The resulting platform is expected to significantly strengthen the service industry in Europe.

Conceptual situation spaces for semantic situation-driven processes

Context-awareness is a highly desired feature across several application domains. Semantic Web Services (SWS) technologies address context-adaptation by enabling the automatic discovery of distributed Web services for a given task based on comprehensive semantic representations. Whereas SWS technology supports the allocation of resources based on semantics, it does not entail the discovery of appropriate SWS representations for a given situation. Describing the complex notion of a situation in all its facets through symbolic SWS representation facilities is a costly task which may never lead to semantic completeness and introduces ambiguity issues. Moreover, even though not any real-world situation completely equals another, it has to be matched to a finite set of parameter descriptions within SWS representations to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) to facilitate the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. CSS enable fuzzy similarity-based matchmaking between real-world situation characteristics and predefined situation descriptions. Following our vision, the latter are part of semantic Situation-Driven Process (SDP) descriptions, which define a composition of SWS Goals suitable to support the course of an evolving situation. Particularly, we refer to the WSMO approach for SWS. Consequently, our approach extends the expressiveness of WSMO by enabling the automatic discovery, composition and execution of achievable goals for a given situation. To prove the feasibility, we apply our approach to the domain of eLearning and provide a proof-of-concept prototype.

Geospatial Data Integration with Semantic Web Services: The eMerges Approach

Geographic space still lacks the semantics allowing a unified view of spatial data. Indeed, as a unique but all encompassing domain, it presents specificities that geospatial applications are still unable to handle. Moreover, to be useful, new spatial applications need to match human cognitive abilities of spatial representation and reasoning. In this context, eMerges, an approach to geospatial data integration based on Semantic Web Services (SWS), allows the unified representation and manipulation of heterogeneous spatial data sources. eMerges provides this integration by mediating legacy spatial data sources to high-level spatial ontologies through SWS and by presenting for each object context dependent affordances. This generic approach is applied here in the context of an emergency management use case developed in collaboration with emergency planners of public agencies.

A Semantic Web Services-based Infrastructure for Context-Adaptive Process Support

Current technologies aimed at supporting processes - whether it is a business or learning process - primarily follow a metadata- and data-centric paradigm. Whereas process metadata is usually based on a specific standard specification - such as the business process modeling notation (BPMN) or the IMS learning design standard - the allocation of resources is done manually at design- time, and the used data is often specific to one process context only. These facts limit the reusability of process models across different standards and contexts. To overcome these issues, we introduce an innovative semantic Web service-based framework aimed at changing the current paradigm to a context-adaptive service-oriented approach. Following the idea of layered semantic abstractions, our approach supports the development of abstract semantic process model-reusable across different contexts and standards - that enables a dynamic adaptation to specific actor needs and objectives. To illustrate the application of our framework and establish its feasibility, we describe a prototypical application in the e-learning domain.