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Providing Intelligent Content by Using Semantic Web and Web Mining

Learn about intelligent content that adapts to user needs and habits by leveraging the Semantic Web and Web Mining techniques. Discover how to create a universal medium for information exchange and extract useful information from web documents.

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Providing Intelligent Content by Using Semantic Web and Web Mining

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  1. Pinar SENKUL METU Computer Eng. Dept. Providing Intelligent Content by Using Semantic Web and Web Mining Providing Intelligent Content - Pinar SENKUL - METU CENG

  2. Providing Intelligent Content - Pinar SENKUL - METU CENG Intelligent Content What is content? Anything published in the web. Context management systems deal with handling organizing and presenting the content.

  3. Providing Intelligent Content - Pinar SENKUL - METU CENG Intelligent Content How about intelligent content? Adaptable content according to the needs and habits of the user.

  4. Providing Intelligent Content - Pinar SENKUL - METU CENG Semantic Web to create a universal medium for information exchange by putting documents with computer-processable semantics on the World Wide Web. It is a vision of web pages that are understandable by computers, so that they can do searching and calling web services automatically in a standardized way.

  5. Providing Intelligent Content - Pinar SENKUL - METU CENG Semantic Web Related standards XML (the basic platform of the semantic web) RDF (Resource Description Language – gives power to build web in a machine-processable manner) OWL (Ontology Web Language – provides a language for defining structured, Web-based ontologies which delivers richer integration and interoperability of data among descriptive communities)

  6. Providing Intelligent Content - Pinar SENKUL - METU CENG Web Mining web mining is to discover interesting patterns from the web data by using data mining techniques. web documents data web structure data web log data user profiles data.

  7. Providing Intelligent Content - Pinar SENKUL - METU CENG Web Mining Web Content Mining (Web content mining is the extraction of useful information from the content of the web documents.) Web Usage Mining (Web usage mining is application of data mining techniques to discover user access patterns from web data.) Web Structure Mining (Web structure mining can be described as discovering web structure and link topology information from web.)

  8. Providing Intelligent Content - Pinar SENKUL - METU CENG Web Usage Mining automatic discovery of user access patterns from one or more Web servers can help to determine to determine the life time value of customers, cross marketing strategies across products, and effectiveness of promotional campaigns how to better structure a Web site in order to create a more effective presence for the organization.

  9. Providing Intelligent Content - Pinar SENKUL - METU CENG Web Usage Mining Smart-SRA: a new approach for session construction a reactive session reconstruction algorithm. Time and navigation oriented heuristics are used with the site topology. improvement on the quality on the constructed session “A New Approach for Reactive Web Usage Data Processing", by M. A. Bayir et. al., ICDE-WIRI, April 2006.

  10. Providing Intelligent Content - Pinar SENKUL - METU CENG Web Usage Mining Enhancement with Semantic Information • consider the semantic information of the visited sites in site construction and finding associations • New and semantically correct associations and profiles can be extracted

  11. Providing Intelligent Content - Pinar SENKUL - METU CENG Multi-relational Data Mining • Finding associative rules over multiple relations (tables) • Integrating data from multiple tables into a single table can cause loss of semantics and information • New techniques for multiple relations. • Inductive Logic Programming (ILP) is a frequently used approach. • Used in semantic web mining for ontoloy extraction

  12. Providing Intelligent Content - Pinar SENKUL - METU CENG Multi-relational Data Mining ILP – based approach: • 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

  13. Providing Intelligent Content - Pinar SENKUL - METU CENG Multi-relational Data Mining • A new ILP-based association rule discovery system • No negative examples • Purely relational • Rules generated under less language bias and user-defined declarations “A hybrid technique for multi-relational rule mining”, Seda Daglar-Toprak et.al, Technical Report, 2005.

  14. Providing Intelligent Content - Pinar SENKUL - METU CENG Multi-relational Data Mining • Can be used for extracting/generating the semantics of the personalized content

  15. Providing Intelligent Content - Pinar SENKUL - METU CENG Providing Intelligent Content • A framework for intelligent content generation that makes use of • Semantically extended session generation and web mining approach • ILP-based multi-relational association rule mining for generating/extending the semantics of the content • Applicable to various domains

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