Knowledge Discovery for Semantic Web

Knowledge Discovery is traditionally used for analysis of large amounts of data and enables addressing a number of tasks that arise in Semantic Web and require scalable solutions. Additionally, Knowledge Discovery techniques have been successfully applied not only to structured data, i.e., databases but also to semi-structured and unstructured data including text, graphs, images and video. Semantic Web technologies often call for dealing with text and sometimes also graphs or social networks. This chapter describes research approaches that are adopting knowledge discovery techniques to address semantic Web and presents several publicly available tools that are implementing some of the described approaches.

[1]  Dunja Mladenic,et al.  Visualization of Text Document Corpus , 2005, Informatica.

[2]  G Salton,et al.  Developments in Automatic Text Retrieval , 1991, Science.

[3]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[4]  Dunja Mladenic,et al.  Visualization of News Articles , 2004, Informatica.

[5]  Christos Faloutsos,et al.  Monitoring Network Evolution using MDL , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[6]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[7]  Olatz Ansa,et al.  Enriching very large ontologies using the WWW , 2000, ECAI Workshop on Ontology Learning.

[8]  Gerard Salton,et al.  Improving Retrieval Performance by Relevance Feedback , 1997 .

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[10]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[11]  Achim Rettinger,et al.  Towards Machine Learning on the Semantic Web , 2008, URSW.

[12]  Gilles Bisson,et al.  Designing Clustering Methods for Ontology Building - The Mo'K Workbench , 2000, ECAI Workshop on Ontology Learning.

[13]  Brian Matthews,et al.  Semantic Web Technologies , 2005 .

[14]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[15]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[16]  Dunja Mladenic,et al.  Efficient Visualization of Large Text Corpora , 2002 .

[17]  Dunja Mladenic,et al.  Turning Yahoo to Automatic Web-Page Classifier , 1998, European Conference on Artificial Intelligence.

[18]  Dunja Mladenic,et al.  Simple classification into large topic ontology of web documents , 2005 .

[19]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[20]  Ian Witten,et al.  Data Mining , 2000 .

[21]  M. Grobelnik,et al.  Evaluation of Semi-Automatic Ontology Generation in Real-World Setting , 2007, 2007 29th International Conference on Information Technology Interfaces.

[22]  Dunja Mladenic,et al.  Knowledge Discovery for Ontology Construction , 2006 .

[23]  Andreas Wierse,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[24]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[25]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[26]  Marko Grobelnik,et al.  Contextualizing Ontologies with OntoLight: A Pragmatic Approach , 2008, Informatica.

[27]  Dunja Mladenic,et al.  Background knowledge for ontology construction , 2006, WWW '06.

[28]  Philip K. Chan,et al.  Learning implicit user interest hierarchy for context in personalization , 2003, IUI.

[29]  Dunja Mladenic,et al.  Visualizing Very Large Graphs Using Clustering Neighborhoods , 2004, Local Pattern Detection.

[30]  Dunja Mladenic,et al.  Using Text Mining and Link Analysis for Software Mining , 2007, MCD.

[31]  B. Fortuna,et al.  Using DMoz for constructing ontology from data stream , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[32]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[33]  Dunja Mladenic,et al.  Automatic Evaluation of Ontologies , 2007 .

[34]  Daphne Koller,et al.  Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.

[35]  Philipp Cimiano,et al.  Ontology Learning from Text: Methods, Evaluation and Applications , 2005 .