knowledge based personalization by wonjung kim. outline introduction background – infoquilt system...
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Knowledge based Knowledge based PersonalizationPersonalization
byWonjung Kim
OutlineOutline
Introduction Background – InfoQuilt system Personalization in InfoQuilt Related Work Conclusions and Future Work
IntroductionIntroduction
Semantic web - components Semantics of data Semantics of human’s interest
Personalization is a part of the second component
Background – the InfoQuilt Background – the InfoQuilt systemsystem
Semantics based information processing IScape : Information correlation Knowledge sharing based on multiple
ontologies
Background – Overall Background – Overall ArchitectureArchitecture
server
Background – Architecture of a Background – Architecture of a PeerPeer
Personalized Knowledge BasePersonalization AgentIScape Execution
Background – Personalized Background – Personalized Knowledge BaseKnowledge Base
Shared ontologies Personalized ontologies
PersonalizationPersonalization – in InfoQuilt system – in InfoQuilt system
Representation of user profiles Personalization Techniques Personalization Algorithm Examples
Representation of user profilesRepresentation 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
Personalization TechniquesPersonalization 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
Personalization TechniquesPersonalization Techniques- Query relationships- 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)
Personalization TechniquesPersonalization Techniques- Query relationships- Query relationships
Team Flight Query Basketball Football Football
Date Nov. 16, 2001 Nov. 17, 2001 Nov. 19, 2001 Dec. 1, 2001
Location Atlanta, GA Athens, GA Springfield, MA Athens, GA
Query Relationships: Flight UGAFootball, Flight UGABasketball
Query: “bulldog schedule”
Personalization AlgorithmPersonalization Algorithm
Technique Case 1 Case 2
Keywords Matched
Profiles Matched
Knowledge of Latest Context
Frequency of Querying a Domain
Query Relationships
Distance from a Domain of Interest
Personalization AlgorithmPersonalization Algorithm
Technique Case 1 Case 2
1 Keywords Matched 0.4 0.5
2 Profiles Matched 0.2 -
3 Query Relationships 0.15 0.35
4 Frequency of Querying a Domain 0.1 -
5 Knowledge of Latest Context 0.1 0.1
6 Distance from a Domain of Interest 0.05 0.05
These weights are configurable
ExamplesExamplesPersonalized Knowledge Base
Example 1 Example 1 – without profile information (first Query)– without profile information (first Query)
Example 1 – keyword matchingExample 1 – keyword matchingOntologies 1 2 3 4 5 6 Total
UGAFootball 0.5*1.0 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.5
UGABasketball 0.5*1.0 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.5
UGAHockey 0.5*1.0 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.5
JCBulldogs 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
CollegeSports 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
AnimalBulldogs 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
CollegeNews 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
CollegeBasketball 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
USCNewspaper 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
CollegeFootball 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
USCBasketball 0.5*0.5 0.0*0.0 0.1*0.0 0.0*0.0 0.35*0.0 0.05*0.0 0.25
Example 2 – use of user profileExample 2 – use of user profile
Ontologies 1 2 3 4 5 6 Total
UGAFootball 0.4*1.0 0.2*1.0 0.1*1.0 0.1*1.0 0.15*0.0 0.05*1.0 0.85
UGAHockey 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.015625 0.40078
UGABasketball 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.015625 0.40078
AnimalBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
JCBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
P1 <bulldogs, UGAFootball, 2, true, Iscape1>
Query: “bulldogs”
Example 3 – latest contextExample 3 – latest contextP1 <bulldogs, UGAFootball, 10, false, Iscape1>
P2 <bulldogs, UGABasketball, 2, true, Iscape2>
Query: “bulldogs”
Ontologies 1 2 3 4 5 6 Total
UGAFootball 0.4*1.0 0.2*0.5 0.1*0.0 0.1*0.83 0.15*0.0 0.05*1.0 0.633
UGAHockey 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.015625 0.40078
UGABasketball 0.4*1.0 0.2*0.5 0.1*1.0 0.1*0.167 0.15*0.0 0.05*1.0 0.667
AnimalBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
JCBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
Example 4 – query relationshipExample 4 – query relationship
Ontologies 1 2 3 4 5 6 Total
UGAFootball 0.4*1.0 0.2*0.5 0.1*0.0 0.1*0.545 0.15*1.0 0.05*1.0 0.7545
UGAHockey 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.015625 0.40078
UGABasketball 0.4*1.0 0.2*0.5 0.1*1.0 0.1*0.454 0.15*0.0 0.05*1.0 0.6954
AnimalBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
JCBulldogs 0.4*1.0 0.2*0.0 0.1*0.0 0.1*0.0 0.15*0.0 0.05*0.000244 0.4000122
Team Flight Query UGABasketball UGAFootball UGAFootball
Date Nov. 29, 2001 Nov. 29, 2001 Nov. 30, 2001 Dec. 30, 2001
Location Atlanta, GA Springfield, MA Athens, GA Athens, GA
Example 5 – new query termExample 5 – new query term
Ontologies 1 2 3 4 5 6 Total
USCFootball 0.5*1.0 0.1*0.0 0.35*0.0 0.05*0.125 0.50625
USCHockey 0.5*1.0 0.1*0.0 0.35*0.0 0.05*0.015625 0.50078
USCBasketball 0.5*1.0 0.1*0.0 0.35*0.0 0.05*0.125 0.50625
USCNewsPaper 0.5*1.0 0.1*0.0 0.35*0.0 0.05*0.0009765 0.5000488
P1 <bulldogs, UGAFootball, 12, false, Iscape1>
P2 <bulldogs, UGABasketball, 10, true, Iscape2>
P3 <travel, AirTravel, 2, true, Iscape3>
Query: “gamecocks”
Related WorkRelated 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
Related Work…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
Related Work…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
ConclusionConclusion
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
Future WorkFuture 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.
Future WorkFuture 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.
Thank You!Thank You!