A key argument for modeling knowledge in ontologies is the easy
-use and re-engineering of the knowledge. However, beside
nsistency checking, current ontology engineering tools provide
ly basic functionalities for analyzing ontologies. Since
tologies can be considered as (labeled, directed) graphs, graph
alysis techniques are a suitable answer for this need. Graph
alysis has been performed by sociologists for over 60 years, and
sulted in the vivid research area of Social Network Analysis
NA). While social network structures in general currently receive
gh attention in the Semantic Web community, there are only very
w SNA applications up to now, and virtually none for analyzing the
ructure of ontologies.

e illustrate in this paper the benefits of applying SNA to
tologies and the Semantic Web, and discuss which research topics
ise on the edge between the two areas. In particular, we discuss
w different notions of centrality describe the core content and
ructure of an ontology. From the rather simple notion of degree
ntrality over betweenness centrality to the more complex
genvector centrality based on Hermitian matrices, we illustrate
e insights these measures provide on two ontologies, which are
fferent in purpose, scope, and size.

Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.