%0 
%0 Book Section
%A Baldi, Pierre; Frasconi, Paolo & Smyth, Padhraic
%D 2003
%T Modeling the Internet and the Web: Probabilistic Methods and Algorithms
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%B Modeling the Internet and the Web: Probabilistic Methods and Algorithms
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%I Wiley
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%K KI2007WebMining dm kdd mining ml web 
%X Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet. ? Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level. ? Takes a modern approach based on mathematical, probabilistic, and graphical modeling. ? Provides an integrated presentation of theory, examples, exercises and applications. ? Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.
%Z 
%U http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470849061.html
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%0 
%0 Conference Proceedings
%A Begelman, Grigory; Keller, Philipp & Smadja, Frank
%D 2006
%T Automated Tag Clustering: Improving search and exploration in the tag space
%E 
%B Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland
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%K KI2007WebMining clustering search tagging 
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%0 Journal Article
%A Berendt, B.; Hotho, A.; Mladenic, D.; van Someren, M.; Spiliopoulou, M. & Stumme, G.
%D 2004
%T Web Mining: From Web to Semantic Web
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%A Berendt, B.; Hotho, A. & Stumme, G.
%D 2002
%T Towards Semantic Web Mining.
%E 
%B Proc Int. Semantic Web Conference
%C Sardinia, Italy
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%A Brin, Sergey & Page, Lawrence
%D 1998
%T The Anatomy of a Large-Scale Hypertextual Web Search Engine
%E 
%B Computer Networks and ISDN Systems
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%V 30
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%N 1-7
%P 107--117
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%0 Journal Article
%A Chakrabarti, S.
%D 2002
%T Mining the Web: Discovering Knowledge from Hypertext Data
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%A Hoser, Bettina; Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Semantic Network Analysis of Ontologies
%E Sure, York & Domingue, John
%B The Semantic Web: Research and Applications
%C Heidelberg
%I Springer
%V 4011
%6
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%P 514-529
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%S LNAI
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%K 2006 KI2007WebMining myown nepomuk ontology semantic sna socialnetworkanalysis web 
%X 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.
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf
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%0 Conference Proceedings
%A Richardson, Matthew; Prakash, Amit & Brill, Eric
%D 2006
%T Beyond PageRank: machine learning for static ranking.
%E Carr, Les; Roure, David De; Iyengar, Arun; Goble, Carole A. & Dahlin, Michael
%B WWW
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%I ACM
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%P 707-715
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%F conf/www/RichardsonPB06
%K KI2007WebMining learning pagerank rank regression 
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%U http://dblp.uni-trier.de/db/conf/www/www2006.html#RichardsonPB06
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%0 Journal Article
%A Srivastava, J.; Cooley, R.; Deshpande, M. & Tan, P.-N.
%D 2000
%T Web usage mining: discovery and application of usage patterns from web data
%E 
%B SIGKDD Explorations
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%N 2
%P 12--23
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%A 
%D 2000
%T Web Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers
%E Masand, Brij M. & Spiliopoulou, Myra
%B WEBKDD
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%I Springer
%V 1836
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%S Lecture Notes in Computer Science
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