BibSonomy publications for /user/kde/KI2007WebMiningWed Jul 25 11:58:51 CEST 2007Web Mining: From Web to Semantic Web2004KI2007WebMining Wed Jul 25 11:55:54 CEST 2007Mining the Web: Discovering Knowledge from Hypertext Data2002KI2007WebMining Mon Jul 16 12:17:13 CEST 2007HeidelbergThe Semantic Web: Research and ApplicationsJune514-529LNAISemantic Network Analysis of Ontologies40112006sna 2006 nepomuk socialnetworkanalysis semantic web myown KI2007WebMining ontology 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.Mon Jul 16 12:13:43 CEST 2007Computer Networks and ISDN SystemsApril1-7107--117{T}he {A}natomy of a {L}arge-{S}cale {H}ypertextual {W}eb {S}earch {E}ngine301998KI2007WebMining imported Mon Jul 16 12:12:51 CEST 2007Sardinia, ItalyProc Int. Semantic Web ConferenceTowards Semantic Web Mining.2002KI2007WebMining imported Mon Jul 16 11:36:54 CEST 2007WEBKDDLecture Notes in Computer ScienceWeb Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers18362000KI2007WebMining imported Mon Jul 16 11:35:14 CEST 2007Modeling the Internet and the Web: Probabilistic Methods and Algorithms4AprilModeling the Internet and the Web: Probabilistic Methods and Algorithms2003mining dm web KI2007WebMining ml kdd 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.Fri Jul 13 12:23:55 CEST 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandAutomated Tag Clustering: Improving search and exploration in the tag space2006tagging search KI2007WebMining clustering Fri Jul 13 11:58:51 CEST 2007SIGKDD Explorations212--23Web usage mining: discovery and application of usage patterns from web data12000KI2007WebMining imported Fri Jul 13 11:57:23 CEST 2007WWWconf/www/2006707-715Beyond PageRank: machine learning for static ranking.2006rank pagerank regression KI2007WebMining learning Fri Jul 13 11:56:20 CEST 2007Brisbane, AustraliaProceedings of the 7th International World Wide Web Conference161--172The PageRank citation ranking: Bringing order to the Web1998KI2007WebMining imported Fri Jul 13 11:55:42 CEST 2007Communications of the ACM8142--151Automatic personalization based on Web usage mining432000KI2007WebMining imported Fri Jul 13 11:54:46 CEST 2007New York TimesNovember 12http://www.nytimes.com/2006/11/12/business/12web.html?ex=1320987600\&en=254d697964cedc62\&ei=5088Entrepreneurs See a Web Guided by Common Sense2006KI2007WebMining imported Fri Jul 13 11:54:07 CEST 2007Ontology Learning for the Semantic Web2002KI2007WebMining imported Fri Jul 13 11:53:11 CEST 2007Data Mining and Knowledge Discovery83--105Efficient adaptive-support association rule mining for recommender systems62002KI2007WebMining imported Fri Jul 13 11:52:34 CEST 2007SIGMOD Record284--93A Brief Survey of Web Data Extraction Tools312002KI2007WebMining imported Fri Jul 13 11:51:56 CEST 2007Piscataway, NJ, USAIEEE Transactions on Knowledge and Data EngineeringMember-Chia-Hui Chang and Member-Moheb Ramzy Girgis101411--1428A Survey of Web Information Extraction Systems182006information ie survey KI2007WebMining extraction Fri Jul 13 11:51:15 CEST 2007Boston, MAProc. WebKDD 200095--104Navigation analysis tool based on the correlation between contents distribution and access patterns.2000KI2007WebMining imported Fri Jul 13 11:50:30 CEST 2007Hong KongProceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)December907-911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices20062006 trias nepomuk myown KI2007WebMining l3s iccs_example wp5 fca algorithm 12 Fri Jul 13 11:48:34 CEST 2007LDV Forum - GLDV Journal for Computational Linguistics and Language TechnologyMAY119-62 A Brief Survey of Text Mining202005mining text overview 2005 survey myown KI2007WebMining ontology SumSchool06