a method for detecting behavior-based user profiles in collaborative ontology engineering

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29-10-2014 pag. 1 A Method for Detecting Behavior- Based User Profiles in Collaborative Ontology Engineering Sven Van Laere, Ronald Buyl and Marc Nyssen 29-10-2014 @ ODBASE, OTM 2014

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Page 1: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

29-10-2014 pag. 1

A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology

Engineering

Sven Van Laere, Ronald Buyl and Marc Nyssen

29-10-2014 @ ODBASE, OTM 2014

Page 2: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

22-9-2015 pag. 2

Overview

• Motivation

• User profiling• … definition

• … in the research field

• Ontology engineering

• Method

• Use Case

• Conclusions and Future work

Page 3: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Motivation

• Types of users are not known beforehand

• Ontology engineering is far from trivial

• Most methods and tools use a set of predefined roles

• Depend on the ontology project and interests of a user

• Assigning based on previous experiences, confidence andreliability in user

Roles and Responsibilities

vs

Users

Page 4: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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User profile

• Definition

• … is a model of a user’s interest and preferences which an agent can use to assist a user’s activity based on inferring observable information1,2

[1] D. Godoy and A. Amandi. User Profiling in Personal Information Agents: a Survey. (2005)

[2] I. Zukerman and D. Albrecht. Predictive Statistical Models for User Modeling. (2011)

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User profile

• In the research field• Fields

• News

• Internet browsing

• Mail

• E-commerce

• Computer supported collaborative work (CSCW)

• …

• Approaches• Knowledge based user profiling

• Behaviour based user profiling

Page 6: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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User profile

• Behaviour based user profiling• Behavioural dimensions

• Focus dispersion

• Engagement

• Contribution

• Initiation

• Content Quality

• Popularity

“How to determine user role/profile basedon the type of input of a user?”

Page 7: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Ontology Engineering

• GOSPL• Grounding Ontologies with Social Processes and

Natural Language

• Chosen for its explicit social interactions• Communities promoted to first class citizens

• Use of natural definitions (called ‘glosses’)

• Concepts are represented

• Formally => lexon

• Informally => gloss

Page 8: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Ontology Engineering

• GOSPL

Page 9: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Ontology Engineering

• Interactions in GOSPL tool

•Acting like forum

•Difference between forum and O.E.:• Closer

• Goal-oriented

• Deadline driven

Page 10: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Method

Page 11: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Method – Extraction Phase

• Apply D2RQ mappingof GOSPL ontology

Socialinteraction(sioc:Item)

Vote Sioc:Post Reply …

Glossinteractions

Glossinteractions

ADD gloss

UPDATEgloss

DELETE gloss

Socialinteraction(sioc:Item)

Vote Sioc:Post Reply …

Glossinteractions

Glossinteractions

ADD gloss

UPDATEgloss

DELETE gloss

Social interaction(sioc:Item)

Vote sioc:Post Reply …

Glossinteractions

Lexoninteractions

ADD gloss

UPDATEgloss

DELETE gloss

…ADD gloss

UPDATEgloss

DELETE gloss

Page 12: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Method – Manipulation Phase

• Standardize dataset

• Principal Component Analysis (PCA)

• Transformation of variables (ortogonal)

• Reduce dimensionality

• Compose new matrix

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Method – Clustering Phase

• K-means clustering

• ANOVA

• Silhouette coefficients

• Take best result => different profiles

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Use Case

123456789

1011121314

1516171819202122232425262728

2930313233343536373839404142

91

3642

22303941

2152511

4161718

10262714

20131219

382128

6

37735

333231

8

34352423

4029

Work in teams

Page 15: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Use Case

[A] gloss interactions

[B] lexon interactions

[C] constraint interactions

[D] supertype interactions

[E] gloss equivalence

interactions

[F] synonym interactions

[G] general request

interactions

[H] reply interactions

[I] closes of topics

[J] vote interactions

1st iteration

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Use Case

• Standardize data (z-score)

• PCA transformations• 95% of variance

• Iterative process

• Original: 42 users 10 dimensionsAfter PCA: 42 users 05 dimensions

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Use Case

• K-mean clustering

• Silhouette calculations

• ANOVA testing

• α = 0.95

Page 18: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Use Case

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Use Case

91

3642

22303941

2152511

4161718

10262714

20131219

382128

6

37735

333231

8

34352423

4029

91

3642

22303941

2152511

4161718

10262714

20131219

382128

6

37735

333231

8

34352423

4029

Cluster 1

Cluster 4

Cluster 2

Cluster 3

Cluster 5

Page 20: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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Conclusions and Future Work

• Conclusions

• Demonstration of method for UP:• Semantic mapping (SIOC)

• Extract data

• Standardize data

• PCA to reduce dimensionality

• K-means clustering

• Silhouette coefficients and ANOVA testing

• 5 clusters based on behaviour

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Conclusions and Future Work

• Discussion & future work

• Sensitive to active and passive users

• Combine with classic behaviouraldimensions

• Validation cluster quality• Dunn index

• Davies-Bouldin index

• C-index

• Iterate process and re-evaluate

Page 22: A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

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References

• D. Godoy and A. Amandi. User Profiling in Personal Information Agents: a Survey. Knowledge Engineering Review, 20(4):329–361, 2005.

• C. Debruyne and R. Meersman. GOSPL: A method and tool for fact-oriented hybrid ontology engineering. In: T. Morzy, T. Härder, R. Wrembel (eds.) ADBIS 2012.LNCS, vol. 7503, pp. 153–166. Springer, Heidelberg (2012)

• M. Rowe, M. Fernandez, S. Angeletou, and H. Alani. Community Analysis through Semantic Rules and Role Composition Derivation. Web Semantics: Science, Services and Agents on the World Wide Web, 18(1):31–47, 2013.

• I. Zukerman and D. Albrecht. Predictive Statistical Models for User Modeling. User Modeling and User-Adapted Interaction, 11(1-2):5–18, 2001.

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THANK YOU!