A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

We provide a new method for systematically structuring the top-down level of ontologies. It is based on an interactive, top--down knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.

Topics in education are changing with an ever faster pace. E-Learning resources tend to be more and more decentralised. Users need increasingly to be able to use the resources of the web. For this, they should have tools for finding and organizing information in a decentral way. In this, paper, we show how an ontology-based tool suite allows to make the most of the resources available on the web.

Increases in product complexity, the move toward globalization, the emergence of virtual organizations, and the increase in focus on customer orientation all demand a more thorough and systematic approach to managing knowledge. Knowledge management is a major issue for human resource management, enterprise organization, and enterprise culture. But IT plays a major supporting role in managing knowledge.The authors present an approach for ontology-based knowledge management that includes a suite of tools as well as a methodology for developing ontology-based KM systems. Their approach builds on the distinction between knowledge process and knowledge metaprocess. They illustrate their methodology with CHAR, a knowledge management system for corporate history analysis.The challenges lie in analyzing KM processes and dynamically updating the KM solution. The authors' framework is designed to help leverage these evolving systems by providing a concise view of the problem.