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This section will focus on other generally interesting topics around ontology alignment for the near and further future. Finally, some general comments about the theoretic quality that can be achieved will be given. [ABSTRACT FROM AUTHOR]

A second project relying on the developed alignment approaches is SEKT. Again the overall project will be presented first. The chapter is then going to continue with the description of the individual case studies and their need for ontology alignment. [ABSTRACT FROM AUTHOR]

Due to the many facets of knowledge integration one cannot expect the goal of ontology alignment to be completed within the next years. This part, in first place, will give directions on extending the presented methodology. In specific, it will generalize the ontology alignment approach to general structure alignment. Further, it will allow more complex alignments than the current one-to-one identity alignments. [ABSTRACT FROM AUTHOR]

This book extensively addressed the topic of ontology alignment. To make the contents easily memorizable for the reader we will once again summarize the main contributions in this chapter. Based hereon we will examine whether the goals defined in the beginning have been reached. Final statements conclude this work and take up any remaining loose ends. [ABSTRACT FROM AUTHOR]

This chapter will show the application of the developed alignment techniques within the project SWAP. After a short general description of the project, its two case studies will be presented each with respective sections on ontology alignment (Haase et al., 2004a; Tempich et al., 2004). These show that the developed ideas actually do carry over to practice. [ABSTRACT FROM AUTHOR]

After the creation of the new approaches, we now need to deploy them. Therefore, we will first show how these are implemented. This chapter is split into three implementations of the alignment approach: a basic infrastructure for ontology alignment and mapping (FOAM), Ontology Mapping based on Axioms (OMA), and the OntoStudio plug-in OntoMap. All of them have specific strengths, though the most important implementation undoubtedly is FOAM. We will refer to it in the subsequent practical applications. [ABSTRACT FROM AUTHOR]

We have originally identified specific core requirements for different use cases. Based on our alignment approach from the previous chapter we will now step-wise address these requirements. Specific extended methods are presented in the following sections. These include efficiency increasing methods, machine learning aid, intelligent user interaction, and adaptive strategies for underlying use cases. Each section will also include an evaluation. Finally, we present a strategy combining all these individual improvements. This sophisticated approach will be the final result of all these efforts. It shall serve to practically align real world ontologies, being open for future extensions. We have motivated that such an approach is a key step towards knowledge integration in information systems. [ABSTRACT FROM AUTHOR]

After the foundations, the next step in research methodology is the actual creative and innovative one. This central chapter of the book will now develop a new approach for ontology alignment based on the previous findings. In fact, this will be achieved through several elements. First, we will shape a general underlying process for alignment. Specific methods for each process step are then going to lead to a concise basic approach (Ehrig and Sure, 2004a). The modular composition makes it easy to follow the new ideas in this work. We will show that already existing approaches also fit this process very well. Furthermore, in later chapters different methods in the process are going to be substituted to better meet the previously identified requirements. An additional important section for proving the value of this work is a thorough evaluation. After we will have introduced the fundamentals of ontology alignment evaluation, we will directly evaluate the novel approach and show its strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Ontology alignment touches many related fields. Data integration but also ontology integration are not new topics. To ensure the reuse of existing ideas an extensive investigation on related work is necessary before an own innovative approach is developed. This chapter starts with theoretic considerations on alignment. It will then focus on other actual approaches for ontology alignment. We will reach out to related fields, in specific the integration efforts of the database community, which has been approaching this issue for many years. A good overview of related work is also given in de Bruijn et al. (2006). [ABSTRACT FROM AUTHOR]

In this book, ontology alignment is supposed to suit specific practical scenarios and not only a high-level theory. This was one of the basic claims presented in the beginning. For this reason, the next sections will examine concrete use cases and derive requirements for ontology alignment from them. As they make clear in which direction the research needs to head, these requirements are another important part of the foundations. Based on these findings, new approaches for ontology alignment will then be possible. [ABSTRACT FROM AUTHOR]

Any good research needs a strong foundation. This chapter lays these foundations for the book. One characteristic of original research is the lack of a standardized vocabulary. Therefore, we will first explain the terminology of ontology and alignment. Then the concept of similarity and its meaning for ontologies will be described, on both an abstract and a concrete level with examples. Only with a thorough understanding of the main expressions, it is possible to follow the ideas of the succeeding chapters. [ABSTRACT FROM AUTHOR]

Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of "who" in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective. [ABSTRACT FROM AUTHOR]

With progress in technology, information management systems are transitioning from storing well defined entities and relationships to the challenge of managing multifarious heterogeneous data. Underlying such data is often a rich diversity of information with emergent semantics. Recognizing this characteristic is essential to executing the transition from data to knowledge. In this context, this chapter presents the paradigm of experiential environments for facilitating user- data interactions in information management systems. Experiential environments emphasize obtaining information and insights rather than pure data lookup. To facilitate this aim, the paradigm utilizes the sentient nature of human beings, their sensory abilities, and interactive query-exploration-presentation interfaces to experience and assimilate information. [ABSTRACT FROM AUTHOR]