Hoser, B.; Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Semantic Network Analysis of Ontologies. In: Sure, Y. & Domingue, J. (Hrsg.): The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 514-529
[Volltext]
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.

Stumme, G.; Hotho, A. & Berendt, B.: Semantic Web Mining - State of the Art and Future Directions. In: Journal of Web Semantics 4 (2006), Nr. 2, S. 124-143
[Volltext]
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
b itself.
e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
tegration could be profitable.