## 1 Introduction  
Representation and use of knowledge is gaining importance in various computer science disciplines. Based on ontologies, the so-called semantic technologies allow the externalization of knowledge and the computation with knowledge in huge, decentralized systems, for example, the Web. Herein, an ontology is an abstract model representing the real world [1] consisting of a formally organized set of concepts (or entities), attributes (which define properties of concepts), relations (which define relationships between concepts) and rules (or axioms, which define boundary conditions on entities, attributes, and relations). In contrast to traditional technologies, where knowledge is typically woven into data and software, systems based on semantic technologies employ ontologies as \"external\" carriers of knowledge. The main advantage of this approach is that ontologies make sharing and reuse of knowledge possible. However problems arise when integration of systems is attempted which use different ontologies to express \"the same\" knowledge. Since ontologies are merely a representation of reality, they will differ due to different requirements, vocabularies, modeling conventions, and also because of the subjective views of knowledge possessed by the engineers who created them. Ontology mediation is an umbrella term covering various techniques involved in overcoming differences between ontologies with the aim of allowing their reuse. Mediation is central to enabling collaboration and integration of systems using different ontologies. This is because interoperability between ontologies and between the systems that use them, become possible only when different ontologies are brought into mutual accord. Application domains include semantic service integration, semantic agent information exchange, ontology-driven data integration, information retrieval from semantically described heterogeneous databases, personalized information delivery, and many others. Ontology mediation consists of the following [2] (note that in the literature, differences in the definitions of these terms may be encountered):  
* **Ontology mapping** deals with relating concepts from different ontologies and is typically concerned with the representation and storage of mappings between the concepts. * **Ontology alignment** is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. The ontologies themselves are unaffected by the alignment process. * **Ontology merging** deals with producing a completely new ontology that ideally captures all knowledge from the original ontologies. Ontology alignment techniques are of particular importance because the manual creation of mappings between concepts is excessively time consuming for all but very small ontologies and therefore, not generally feasible. On the other hand, both alignment and merging approaches enable interoperability between different ontologies. However, alignment is far less complex than merging since creating and maintaining links between concepts is easier and less resource-intensive than producing a completely new, consistent ontology from the original ones. Although fully automatic ontology alignment might appear as the solution of choice for the interoperability of semantic systems, results provided by fully automatic methods are rarely of sufficient quality. The challenges faced by fully automatic methods are manifold, including vocabulary differences (e.g., due to synonymy andhomonymy), modeling differences (e.g., due to different model granularity or different attribute formats) and different points of view on the modeled reality. To overcome those challenges, semi-automatic approaches have been proposed with the goal of including the knowledge and the capabilities of human experts in the alignment process. Quite obviously, for effective semi-automatic systems, the crucial point is the design of the user interface.