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.
Auch erschienen in: Sure, York u.a. (Hrsg.): The semantic web. (Lecture notes in computer science ; 4011). Berlin u.a. : Springer, 2006. S. 530-544. ISBN 3-540-34544-2 - 978-3-540-34544-2 (The original publication is available at www.springerlink.com)