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 AgentIScape Execution

7. Background – Personalized Knowledge Base Shared ontologiesPersonalized 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: 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 -keywords matched Not in profiles Query : “Bulldog Football ” Total number of keyword n = 2 Number of keywords matched m BulldogFootball P2p UGABaseball UGABasketball UGAFootball ½½1½½1 collegefootball professionalfootball UGAFootball ½½1½½1

12. Personalization Techniques - profiles matched P 1 P 2 Query: “Bulldog Schedule” P2p UGABaseball UGABasketball UGAFootball collegefootball professionalfootball 0/2 1 0/2 schedule bulldog

13. Personalization Techniques - knowledge about latest context Advantage: take the current ontology of the current query Example: P 1 P 2 P 3 P 4 It shows UGAFootball is the current ontology of the term bulldog”

14. Personalization Techniques - frequency of querying a domain P 1 P 2 P 3 Query: “bulldog football” Matched ontologies: UGAFootball and UGABasketball UGAFootball: (10+12)/(10+12+5) UGABasketball: 5 / (10+12+5)

15. 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)

16. Personalization Techniques - Query relationships TeamFlight QueryBasketballFootball DateNov. 16, 2001Nov. 17, 2001Nov. 19, 2001Dec. 1, 2001 LocationAtlanta, GAAthens, GASpringfield, MAAthens, GA Query Relationships: Flight  UGAFootball, Flight  UGABasketball Query: “bulldog schedule”

17. Personalization Techniques - Personalization Techniques - Distance from a domain of interest The smaller the distance, the more relevant it is likely to be. Example) there is no query history about the term “gamecock” in a user’s profile. P1 Query: “gamecock schedule” P2P  gamecocks, USCFootball USCFootball:1*0.5*0.25 = 0.125 Gamecocks: 1*0.5*0.5*0.5*0.5*0.25*0.25=0. 00390625

18. Personalization Algorithm TechniqueCase 1Case 2 Keywords Matched  Profiles Matched  Knowledge of Latest Context  Frequency of Querying a Domain  Query Relationships  Distance from a Domain of Interest 

19. Personalization Algorithm TechniqueCase 1Case 2 1Keywords Matched 0.40.5 2Profiles Matched 0.2- 3Query Relationships 0.150.35 4Frequency of Querying a Domain 0.1- 5Knowledge of Latest Context 0.1 6Distance from a Domain of Interest 0.05 These weights are configurable

20. Examples Personalized Knowledge Base

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

22. Example 1 – keyword matching Ontologies123456Total UGAFootball0.5*1.00.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.5 UGABasketball0.5*1.00.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.5 UGAHockey0.5*1.00.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.5 JCBulldogs0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 CollegeSports0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 AnimalBulldogs0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 CollegeNews0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 CollegeBasketball0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 USCNewspaper0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 CollegeFootball0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25 USCBasketball0.5*0.50.0*0.00.1*0.00.0*0.00.35*0.00.05*0.00.25

23. Example 2

24. Example 2 – use of user profile Ontologies123456Total UGAFootball0.4*1.00.2*1.00.1*1.0 0.15*0.00.05*1.00.85 UGAHockey0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0156250.40078 UGABasketball0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0156250.40078 AnimalBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122 JCBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122 P 1 Query: “bulldogs”

25. Example 3

26. Example 3 – latest context P 1 P 2 Query: “bulldogs” Ontologies123456Total UGAFootball0.4*1.00.2*0.50.1*0.00.1*0.830.15*0.00.05*1.00.633 UGAHockey0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0156250.40078 UGABasketball0.4*1.00.2*0.50.1*1.00.1*0.1670.15*0.00.05*1.00.667 AnimalBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122 JCBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122

27. Example 4 - query relationship P 1 P 2 P 3 Query: “bulldogs”

28. Example 4

29. Example 4 – query relationship Ontologies123456Total UGAFootball0.4*1.00.2*0.50.1*0.00.1*0.5450.15*1.00.05*1.00.7545 UGAHockey0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0156250.40078 UGABasketball0.4*1.00.2*0.50.1*1.00.1*0.4540.15*0.00.05*1.00.6954 AnimalBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122 JCBulldogs0.4*1.00.2*0.00.1*0.0 0.15*0.00.05*0.0002440.4000122 TeamFlight QueryUGABasketballUGAFootball DateNov. 29, 2001 Nov. 30, 2001Dec. 30, 2001 LocationAtlanta, GASpringfield, MAAthens, GA

30. Example5 – Query with the new term

31. Example 5 – new query term Ontologies123456Total USCFootball0.5*1.00.1*0.00.35*0.00.05*0.1250.50625 USCHockey0.5*1.00.1*0.00.35*0.00.05*0.0156250.50078 USCBasketball0.5*1.00.1*0.00.35*0.00.05*0.1250.50625 USCNewsPaper0.5*1.00.1*0.00.35*0.00.05*0.00097650.5000488 P 1 P 2 P 3 Query: “gamecocks”

32. 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

33. 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

34. 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

35. 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

36. 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.

37. 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.

38. Thank You!