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!