1. Web Mining and Semantic Web Web Mining and Semantic Web Pınar Şenkul senkul@ceng.metu.edu.trMETU Dept. of Computer Engineering

2. Data mining finding interesting trends and patterns in very large databases extract information which is not explicit from the data and implicitly specified also related to artificial intelligence (knowledge discovery and machine learning) and statistics

3. Data Mining Applications Sequential patterns Association rules Clustering and classification

4. Discovering Association Rules Rule: “If i then j” Support: the percentage of records containing both items i and j. Confidence: the percentage of records containing item j among the records containing item i.

5. Multi-Table Association Rule Mining Integrating data from multiple tables into a single table through some preprocessing such as using joins and aggregation –can cause loss of semantics and information Inductive Logic Programming (ILP) –applies directly to the data that is stored on multiple tables

6. ILP Systems Top-down vs. Bottom-up Well-known systems: –CLAUDIEN, ICL, WARMR, TILDE, PROGOL, ALEPH, TERTIUS, FDEP,FOCL,GOLEM, MERLIN, FLIP & SMILES, ATRE, ACL, CHILLIN, FORTE, GBR, LIVE

7. ILP Given: –a set of examples, E –background knowledge, BK –produce a set of relations (clauses) using BK that describe E. Strong language bias : precise syntactical description of acceptable clauses

8. Current Problems ILP-Based Association Rule Discovery System –No negative examples –Purely relational –Using support & confidence –Efficient but restricted with language biases

9. Application Areas Has a verity of application areas Possibly –Web mining –Semantic Web/Ontology Mining

10. Web Mining Using data mining techniques on web data web content mining web structure mining web usage mining

11. Web Usage Mining Discovering usage patterns from the web in order to better understand and serve the needs of users and web-based applications. It includes two important phases: data preprocessing pattern discovery

12. Semantic Web Mining Discovering better patterns by using semantic information Discovering/enhancing the semantic information by the using the extracted patterns.

13. Web Services and Web Service Composition Problem: construction of a complex service from the existing individual atomic services according to user's needs and requirements. The set of existing services is highly dynamic Individual service selection should be ● dynamic ● fulfill user's requirements

14. Web Service Composition ● A uniform framework for modeling and satisfaction of the constraint ● Flexibility for constraint specification ● Automatic selection of concrete services

15. Example ● Wedding Anniversary Celebration – buy a bouquet of flowers – buy earrings or buy a one-day weekend trip – make dinner reservation Constraints: budget ≤ $1000 duration < 5 hours quality(restaurant) ≥ 3-star

16. Architecture

17. Some Problems How to using semantic knowledge for different levels of composition process Web mining for service recommendation utilizing semantic information

18. Some of the FP6-IST projects on data mining –Semantic Interoperability and Data Mining in Biomedicine Semantic Interoperability and Data Mining in BiomedicineSemantic Interoperability and Data Mining in Biomedicine Project Acronym: SEMANTICMINING Action Line: IST-2002-2.3.1.11 eHealth Contract Type: NETWORK OF EXCELLENCE –Semantic Interaction with Music Audio Contents Semantic Interaction with Music Audio ContentsSemantic Interaction with Music Audio Contents Project Acronym: SIMAC Action Line: IST-2002-2.3.1.7 Semantic-based knowledge systems Contract Type: SPECIFIC TARGETED RESEARCH PROJECT –Data Mining Tools and Services for Grid Computing Environments Data Mining Tools and Services for Grid Computing EnvironmentsData Mining Tools and Services for Grid Computing Environments Project Acronym: DATAMININGGRID Action Line: IST-2002-2.3.2.8 Grid based systems for complex problem solving Contract Type: SPECIFIC TARGETED RESEARCH PROJECT –Inductive Queries for Mining Patterns and Models Inductive Queries for Mining Patterns and ModelsInductive Queries for Mining Patterns and Models Project Acronym: IQ Action Line: IST-2002-2.3.4.1 FET - Open Contract Type: SPECIFIC TARGETED RESEARCH PROJECT –GRID ENABLED REMOTE INSTRUMENTATION WITH DISTRIBUTED CONTROL AND COMPUTATION GRID ENABLED REMOTE INSTRUMENTATION WITH DISTRIBUTED CONTROL AND COMPUTATIONGRID ENABLED REMOTE INSTRUMENTATION WITH DISTRIBUTED CONTROL AND COMPUTATION Project Acronym: GRIDCC Action Line: IST-2002-2.3.5 Research Networking Test beds Contract Type: SPECIFIC TARGETED RESEARCH PROJECT

19. Contact Info Pinar SENKUL senkul@ceng.metu.edu.tr http://www.ceng.metu.edu.tr/~karagoz Ismail Hakki TOROSLU toroslu@ceng.metu.edu.tr http://www.ceng.metu.edu.tr/~toroslu http://www.ceng.metu.edu.tr/~e1347145/METU-ISTEC/