1. Towards Semantic Web Mining Bettina Berndt Andreas Hotho Gerd Stumme

2. Semantic Web Mining Combination of Semantic Web and Web Mining Improve Web Mining using Semantic Web Improve Semantic Web using Web Mining

3. Overview Web Mining Extracting Semantics from the Web Exploiting Semantics for Web Mining Mining the Semantic Web Closing the Loop Conclusion/Assessment

4. Web Mining Discovers Local and Global Structure Structured Data Goals Improvement of site design Generate dynamic recommendations Improve marketing Main Areas Web Content Mining Web Structure Mining Web Usage Mining

5. Content Mining Type of Text Mining Uses Tags Detect co-occurrences Event detection Reconstruction of page content Relations in a domain

6. Web Structure Mining WebPages as a whole Uses hyperlinks Identify relevance Single Pages Five types of Web Pages Head Pages Navigation Pages Content Pages Look up Pages Personal Pages

7. Web Usage Mining Request by Visitors Additional Structure Unintended Relationships

8. Web Mining

9. Disadvantages of Web Mining Content/Structure False positives Unused Human understandable Large amount of data Usage Usage tracked by urls General concepts Multiplicity of events and urls

10. Relational Metadata DAMLPROJ COOPERATES- WITH URI-GST URI-SWMining COOPERATES- WITH WORKS-IN PROJECT RESEARCHER PERSON TOP COOPERATES-- WITH TITLE NAME RESEARCHER PERSON Ontology COOPERATES-- WITH Semantic Web Mining WWW - URI-AHO Andreas Hotho cooperateswith (X,Y)  cooperateswith (Y,X) WORKS-IN

11. Outline Web Mining Extracting Semantics from the Web Exploiting Semantics for Web Mining Mining the Semantic Web Closing the Loop Conclusion/Assessment

12. Extracting Semantics Ontology Learning Learn structures of Ontologies Instance Learning Populates the Ontologies

13. Extracting Semantics Ontology Learning Semi-automatic approach Merging FCA-Merge TITANIC Instance Learning Information Extraction

14. Outline Web Mining Extracting Semantics from the Web Exploiting Semantics for Web Mining Mining the Semantic Web Closing the Loop Conclusion/Assessment

15. How can the semantic web help with web mining ?

16. Web Content/Structure Mining Content Mining Preprocess the input data Apply heuristics Creates a cluster Web Structure Mining Page Rank Keyword Analysis CLEVER

17. Conceptual Clustering of Emails (and Bookmarks) using IE and Formal Concept Analysis for supporting navigation and retrieval.

18. Web Usage Mining Goal Better understand user’s tendencies Problem Dynamic pages How to take advantage of this? Generate queries Create usage paths Classification scheme

19. Advantages Structured Model Improve queries Analyze single pages Analyze ontologies Users history

20. Outline Web Mining Extracting Semantics from the Web Exploiting Semantics for Web Mining Mining the Semantic Web Closing the Loop Conclusion/Assessment

21. How can web mining help build the semantic web?

22. Semantic Web/Structure Mining Intertwined Relational Data Mining Looks for patterns Classification, regression, clustering and associations Challenges Scalability Distributed

23. Semantic Web Usage Mining Goal Requested page = ontology entity Log files Advantages Understand search strategies Improve navigation design Personalize

24. Outline Web Mining Extracting Semantics from the Web Exploiting Semantics for Web Mining Mining the Semantic Web Closing the Loop Conclusion/Assessment

25. Mining to Learn Ontologies Establish a concept hierarchy OntEx Determine Association rules Discover combinations of concepts

26. Mining to Learn Ontologies

27. Filling the Ontologies

28. Use Ontology to Mine

29. Conclusion/Assesment Semantic Structures in the Web can help Web mining Web Mining can build the Semantic Web Combine the two together Different Idea Combination of Products

30. Questions?