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Knowledge based Personalization

Knowledge based Personalization. by Wonjung Kim. Outline. Introduction Background – InfoQuilt system Personalization in InfoQuilt Related Work Conclusions and Future Work. Introduction. Semantic web - components Semantics of data Semantics of human’s interest

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Knowledge based Personalization

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  1. Knowledge based Personalization by Wonjung Kim

  2. Outline • Introduction • Background – InfoQuilt system • Personalization in InfoQuilt • Related Work • Conclusions and Future Work

  3. Introduction • Semantic web - components • Semantics of data • Semantics of human’s interest • Personalization is a part of the second component

  4. Background – the InfoQuilt system • Semantics based information processing • IScape : Information correlation • Knowledge sharing based on multiple ontologies

  5. Background – Overall Architecture server

  6. Background – Architecture of a Peer Personalized Knowledge Base Personalization Agent IScape Execution

  7. Background – Personalized Knowledge Base Shared ontologies Personalized ontologies

  8. Personalization – in InfoQuilt system • Representation of user profiles • Personalization Techniques • Personalization Algorithm • Examples

  9. Representation of user profiles • Set of tuples of type <Keyword, Ontology, Frequency, Latest interest, IScape> • Keyword: the term used to query • Ontology: used in IScape • Frequency: frequency of query • Latest interest: boolean value • IScape: the name of the last queried IScape

  10. Personalization Techniques Score can be computed based on a scale of 0..1 • Keywords matched • Profiles matched • Knowledge about latest context • Frequency of querying a domain • Query relationship • Distance from a domain of interest

  11. Personalization Techniques- Query relationships • More concrete than e-commerce market association rules • Buy Cereal  Buy Milk • Query Relationship • if a bulldog football team has a game scheduled, then the user may be interested in attending the game so he may query for flight ticket and vice versa. • Use framework for inter-ontological relationships to define query relationships • spatiallyNear(UGAFootball.gameVenue, Flight.arrivalCity) && temporallyNear(UGAFootball.gameDate, Flight.arrivalDate)

  12. Personalization Techniques- Query relationships • Query Relationships: • Flight  UGAFootball, Flight  UGABasketball • Query: “bulldog schedule”

  13. Personalization Algorithm

  14. Personalization Algorithm These weights are configurable

  15. Examples Personalized Knowledge Base

  16. Example 1 – without profile information (first Query)

  17. Example 1 – keyword matching

  18. Example 2 – use of user profile P1 <bulldogs, UGAFootball, 2, true, Iscape1> Query: “bulldogs”

  19. Example 3 – latest context P1 <bulldogs, UGAFootball, 10, false, Iscape1> P2 <bulldogs, UGABasketball, 2, true, Iscape2> Query: “bulldogs”

  20. Example 4 – query relationship

  21. Example 5 – new query term P1 <bulldogs, UGAFootball, 12, false, Iscape1> P2 <bulldogs, UGABasketball, 10, true, Iscape2> P3 <travel, AirTravel, 2, true, Iscape3> Query: “gamecocks”

  22. Related Work • Features of Knowledge Based personalization in InfoQuilt not supported by any other personalization systems • Keywords and concepts in ontologies are used to locate them • Query relationships between domains identify domains that the user’s profile provides no information for

  23. Related Work… • OBIWAN ( Alexander P, Susan G) • Use a vector space model to classify documents • use length, time, and the strength of match to track users’ interest • myPlanet (Yannis K, John D, Enrico M, Maria V, Simon S) • An ontology-driven personalized news publishing service • Use simple relationships in the ontologies to deliver content that may be of interest to the user

  24. Related Work… • Scalable online personalization on the web (Anindya D, Kaushik D, Debra V, Krithi R, Shamkant N) • Collaborative filtering approach • Action rules and market basket rules • Dynamic profile

  25. Conclusion • Personalization in InfoQuilt • Ontologies in the personalized knowledge base reflect the user’s perception of the domain • Keywords that are specified by the ontology, are useful for identifying other relevant ontologies • A number of techniques combined to help the users find relevant ontologies • Query relationships can identify related domains of interest in the current context of user’s query

  26. Future Work • For each domain, it is possible to identify a set of terms that indicate the context. These can also be used to locate ontologies. • The only type of relationships in the ontologies used for identifying domains that may be of interest to the user is “is-a”. We can explore the user of other types of relationships supported by ontologies • Evaluating query relationships requires work equivalent to evaluating one IScape. Instead, the results from the previous IScape can be cached.

  27. Future Work • Keyword matching can be further given weights depending on which component of ontology the keyword matched. For example, if a keyword matches the name of a class as opposed to description, it should have higher value. • Experimenting with large amount of users and ontologies can help in identifying a reasonable weight assignment for the techniques.

  28. Thank You!

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