Semantic Synchronization in B2B Transactions

Despite the development of e-business standards, the integration of business processes and business information systems is still a non-trivial issue if business partners use different e-business standards for formatting and describing information to be processed. Since those standards can be understood as ontologies, ontological engineering technologies can be applied for processing, especially ontology matching for reconciling them. However, as e-business standards tend to be rather large-scale ontologies, scalability is a crucial requirement. To serve this demand, we present our ORBI Ontology Mediator. It is linked with our Malasco system for partition-based ontology matching with currently available matching systems, which so far do not scale well, if at all. In our case study we show how to provide dynamic semantic synchronization between business partners using different e-business standards without initial ramp-up effort, based on ontological mapping technology combined with interactive user participation.

Data Integration in the Geospatial Semantic Web

Geospatial decision makers have to be aware of the varying interests of all stakeholders. One crucial task in the process is to identify relevant information available from the Web. In this chapter the authors introduce an application in the quarrying domain which integrates Semantic Web technologies to provide new ways to discover and reason about relevant information. The authors discuss the daily struggle of the domain experts to create decision-support maps helping to find suitable locations for opening up new quarries. After explaining how semantics can help these experts, they introduce the various components and the architecture of the software which has been developed in the European funded SWING project. In the last section, the different use cases illustrate how the implemented tools have been applied to real world scenarios.

The Ontological Stance for a Manufacturing Scenario

The semantic integration of software systems can be supported through a shared understanding of the terminology in their respective ontologies. In practice, however, the author is faced with the additional challenge that few applications have an explicitly axiomatized ontology. To address this challenge, we adopt the Ontological Stance, in which we can model a software application as if it were an inference system with an axiomatized ontology, and use this ontology to predict the set of sentences that the inference system determines to be entailed or satisfiable. This chapter gives an overview of a deployment of the Process Specification Language (PSL) Ontology as the interchange ontology for the semantic integration of three manufacturing software applications currently being used in industry—a process modeller, a process planner, and a scheduler.

Service Integration through Structure-Preserving Semantic Matching

The problem of integrating services is becoming increasingly pressing. In large, open environments such as the Semantic Web, huge numbers of services are developed by vast numbers of different users. Imposing strict semantics standards in such an environment is useless; fully predicting in advance which services one will interact with is not always possible as services may be temporarily or permanently unreachable, may be updated or may be superseded by better services. In some situations, characterised by unpredictability, such as the emergency response scenario described in this case, the best solution is to enable decisions about which services to interact with to be made on-the-fly. We propose a method of doing this using matching techniques to map the anticipated call to the input that the service is actually expecting. To be practical, this must be done during run-time. In this case, we present our structure-preserving semantic matching algorithm (SPSM), which performs this matching task both for perfect and approximate matches between calls. In addition, we introduce the OpenKnowledge system for service interaction which, using the SPSM algorithm, along with many other features, facilitates on-the-fly interaction between services in an arbitrarily large network without any global agreements or pre-run-time knowledge of who to interact with or how interactions will proceed. We provide a preliminary evaluation of the SPSM algorithm within the OpenKnowledge framework.

Pillars of Ontology Treatment in the Medical Domain

In this chapter the authors describe the three pillars of ontology treatment in the medical domain in a comprehensive case study within the large-scale THESEUS MEDICO project. MEDICO addresses the need for advanced semantic technologies in medical image and patient data search. The objective is to enable a seamless integration of medical images and different user applications by providing direct access to image semantics. Semantic image retrieval should provide the basis for the help in clinical decision support and computer aided diagnosis. During the course of lymphoma diagnosis and continual treatment, image data is produced several times using different image modalities. After semantic annotation, the images need to be integrated with medical (textual) data repositories and ontologies. They build upon the three pillars of knowledge engineering, ontology mediation and alignment, and ontology population and learning to achieve the objectives of the MEDICO project.

An Ontology-Based GeoDatabase Interoperability Platform

This chapter presents an ontology-based platform enabling automatic translation between a large number of geographical formats and data models. It explains the organizational motivations for developing this system, the technologies used, how its architecture and processing components were developed, what it achieves and where it still needs improvement. Since current off-the-shelf description logic reasoners are unable to process the large ontologies involved in this system, this platform uses a custom mapping algorithm that scales gracefully and still computes the required information to effect translation between supported data formats. The authors believe that the lessons learned during this project and discussed in this chapter will prove especially useful to interoperability practitioners contemplating the use of semantic technologies for enabling large-scale integration across organizational boundaries.

Streamlining Semantic Integration Systems

Yannis Kalfoglou and Bo Hu argue for the use of a streamlined approach to integrate semantic integration systems. The authors elaborate on the abundance and diversity of semantic integration solutions and how this impairs strict engineering practice and ease of application. The versatile and dynamic nature of these solutions comes at a price: they are not working in sync with each other neither is it easy to align them. Rather, they work as standalone systems often leading to diverse and sometimes incompatible results. Hence the irony that we might need to address the interoperability issue of tools tackling information interoperability. Kalfoglou and Hu also report on an exemplar case from the field of ontology mapping where systems that used seemingly similar integration algorithms and data, yield different results which are arbitrary formatted and annotated making interpretation and reuse of the results difficult. This makes it difficult to apply semantic integration solutions in a principled manner. The authors argue for a holistic approach to streamline and glue together different integration systems and algorithms. This will bring uniformity of results and effective application of the semantic integration solutions. If the proposed streamlining respects design principles of the underlying systems, then the engineers will have maximum configuration power and tune the streamlined systems in order to get uniform and well understood results. The authors propose a framework for building such streamlined system based on engineering principles and an exemplar, purpose built system, CROSI Mapping System (CMS), which targets the problem of ontology mapping.

Sharing Resources through Ontology Alignments in a Semantic Peer-to-Peer System

In heterogeneous semantic peer-to-peer systems, peers describe their resources through ontologies that they can adapt to their particular needs. In order to interoperate, queries need to be transformed with respect to alignments between their ontologies before being evaluated. Alignments are thus critical for sharing resources in such systems. The authors report an experiment that explores how such alignments can be obtained in a natural way. In particular, asking users to provide alignments is a heavy constraint that must be relaxed as much as possible. This can be attempted through automatic matching. However, the authors suggest other possible solutions.

XAR

The authors present the XAR framework that allows for free text information extraction and semantic annotation. The language underpinning XAR, the authors argue, allows for the inclusion of probabilistic reasoning with the rule language, provides higher level predicates capturing text features and relationships, and defines and supports advanced features such as token consumption and stratified negotiation in the rule language and semantics. The XAR framework also allows the incorporation of semantic information as integrity constraints in the extraction and annotation process. The XAR framework aims to fill in a gap, the authors claim, in the Web based information extraction systems. XAR provides an extraction and annotation framework by permitting the integrated use of hand-crafted extraction rules, machine-learning based extractors, and semantic information about the particular domain of interest. The XAR system has been deployed in an emergency response scenario with civic agencies in North America and in a scenario with an IT department of a county level community clinic.

CRUZAR

This chapter describes CRUZAR, a Web application that builds custom tourism routes for each visitor to the city of Zaragoza. This application exploits expert knowledge of the tourism domain (captured in rules and ontologies) and consumes a consolidated repository of relevant tourism resources (RDF instances extracted from different legacy databases: historical buildings, museums, public parks, restaurants, cultural events…). User profiles and interests, as well as user-defined constraints, are modeled with an ontology. A semantic matchmaking algorithm is applied to find the most interesting resources for each profile, and a planner organizes the selections into an optimal route. The authors discuss the main challenges and design choices.