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Web Mining

Web Mining. by: Katharotiya Manthan. Overview. Web Mining Semantic Web Ontologies Semantic Web Mining Future Work References. Problems With Web Interaction. Finding Relevant Information Creating New Knowledge using Existing Resources Personlization of Information

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Web Mining

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  1. Web Mining by: Katharotiya Manthan

  2. Overview • Web Mining • Semantic Web • Ontologies • Semantic Web Mining • Future Work • References

  3. Problems With Web Interaction • Finding Relevant Information • Creating New Knowledge using Existing Resources • Personlization of Information • Learning about Consumers or Individual Users

  4. Web Mining • The term created by Orem Etzioni (1996) • Application of Data mining techniques  • Web Mining into Subtasks • Resource finding • Information Selection and pre-processing • Generalization • Analysis

  5. Different Types • Web Usage Mining • Web Content Mining  • Web Structure Mining

  6. Data Mining vs. Web Mining • Traditional data mining • data is structured and relational • well-defined tables, columns, rows, keys, and constraints. • Web data • Semi-structured and unstructured • readily available data • rich in features and patterns

  7. Web Structure Mining • Generate structural summary about the Web site and Web page • Extraction of patterns from the hyperlinks • Mining of the structure of the document

  8. Web Usage Mining • Discovering user ‘navigation patterns’ from web data. • Prediction of user behavior while the user interacts with the web. • Helps to Improve large Collection of resources.

  9. Usage Mining Techniques • Data Preparation • Data Collection • Data Selection • Data Cleaning • Data Mining • Navigation Patterns • Sequential Patterns

  10. Data Mining Techniques • Navigation Patterns • Example: • 70% of users who accessed /company/product2 did so by starting at /company and proceeding through /company/new, /company/products and company/product1 • 80% of users who accessed the site started from /company/products • 65% of users left the site after four or less page references

  11. Cont… • Sequential Patterns • In Google search, within past week 30% of users who visited /company/product/ had ‘camera’ as text. • 60% of users who placed an online order in /company/product1 also placed an order in /company/product4 within 15 days

  12. Web Content Mining • ‘Process of information’ or resource discovery from content of millions of sources across the World Wide Web • E.g. Web data contents: text, Image, audio, video, metadata and hyperlinks • Goes beyond key word extraction, or some simple statistics of words and phrases in documents.

  13. Semantic Web • The Semantic Web is an evolving development of the World Wide Web in which the meaning (semantics) of information and services on the web is defined, making it possible for the web to "understand" and satisfy the requests of people and machines to use the web content.

  14. XML, RDF and Web Data • Structured and Unstructured Data • W3c Standards for RDF • Semantic Web: Different Kinds of databases • Tight Coupling and Loose Coupling

  15. RDF - Resource Description Framework • Data Model consists of three object types: • Resources • Properties • Statements

  16. Example • OraLassila is the creator of the resource http://www.w3.org/Home/Lassila • This sentence has the following parts: •  Subject(Resource)  http://www.w3.org/Home/Lassila   • Predicate (Property)  Creator •  Object (literal)  "OraLassila"

  17. Cont…

  18. Cont…

  19. Ontologies • Ontologies are developed to provide machine-processable semantics of information sources that can be communicated between different agents (software and humans).

  20.  Developing an Ontology  • Defining classes in the ontology, • Arranging the classes in a taxonomic (subclass–superclass) hierarchy • Defining slots and describing allowed values for these slots, • Filling in the values for slots for instances.

  21. Cont…

  22. Semantic Web Mining • Closing the gap between Semantic Web and Web Mining. • Use of ontologies

  23. Mining the Semantic in Web

  24. Evaluation Of Semantic Web Mining

  25. Web Mining Vs. Semantic Web Mining • A Note On E-Commerce

  26. Research initiatives • Vivísimo proposes a clustering approach for web document organization • Haveliwala also propose a methodology for evaluating strategies for similarity search on the Web. • Jaccard coefficient

  27. Future Work • Demonstrating the utility of web mining can be done by making exploratory changes to web sites, e.g., adding links from hot parts of web site to cold parts and then extracting, visualizing and interpreting changes in access patterns.

  28. Conti… • There is often a tension in the design of algorithms between accommodating a wide range of data, or customizing the algorithm to capitalize on known constraints or regularities. • Also web content mining can be introduced to implementations of this architecture.

  29. References • http://en.wikipedia.org/wiki/Web_mining • http://www.engr.sjsu.edu/meirinaki/papers/NEMIS.pdf • http://www.w3.org • http://www.cs.washington.edu/research/projects/WebWare1/www/softbots/papers/agents97.pdf • http://infomesh.net/2001/swintro/ • http://www.ksl.stanford.edu/people/dlm/etai/etai-abstract.html

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