Steffen Staab

Similarity measures play a key role in the Semantic Web perspective. Indeed, most of the ontology related operations such as ontology learning, ontology alignment, ontology ranking and ontology population are grounded on the notion of similarity. In the last few years several similarity functions have been proposed for measuring both concept similarity and ontology similarity.

Abstract Efficient resource retrieval is a crucial issue, particularly in the context of Semantic Web, since forms of reasoning are used for answering requests. Resources are retrieved by performing a match test between each resource description and the query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed in Web ontology languages.

Abstract. Semantic service descriptions are frequently given using expressive ontology languages based on description languages. The expressiveness of these languages, however, often implies problems for efficient service discovery, especially when increasing numbers of services become available in large organizations and on the Web.

A framework for modeling Semantic Web Service is proposed. It is based on Description Logic (DL), hence it is endowed with a formal semantics and, in addition, it allows for expressing constraints in service descriptions of different strengths, ie Hard and Soft Constraints. Semantic service discovery can be performed by matching DL descriptions, expressing both Hard and Soft constraints, and exploiting DL inferences.

A crucial task in process management is the validation of process renements. A process renement is a process description in a more ne-grained representation. The renement is either with respect to an abstract model or with respect to component's principle behaviour model. We dene process renement based on the execution set semantics. Predecessor and successor relations of the activities are described in an ontology in which the renement is represented and validated by concept satisability checking.

Nowadays, geographical coordinates (geo-tags), social annotations (tags), and low-level features are available in large image datasets. In our paper, we exploit these three kinds of image descriptions to suggest possible annotations for new images uploaded to a social tagging system. In order to compare the benefits each of these description types brings to a tag recommender system on its own, we investigate them independently of each other. First, the existing data collection is clustered separately for the geographical coordinates, tags, and low-level features. Additionally, random clustering is performed in order to provide a baseline for experimental results. Once a new image has been uploaded to the system, it is assigned to one of the clusters using either its geographical or low-level representation. Finally, the most representative tags for the resulting cluster are suggested to the user for annotation of the new image. Large-scale experiments performed for more than 400,000 images compare the different image representation techniques in terms of precision and recall in tag recommendation.