Ontology engineering is far from trivial and most collaborative methods and tools start from a predefined set of rules, stakeholders can have in the ontology engineering process. We, however, believe that the different types of user behavior are not known a priori and depend on the ontology engineering project. The detection of such user profiles based on unsupervised learning allows finding roles and responsibilities along peers in a collaborative setting. In this paper, we present a method for automatic detection of user profiles in a collaborative ontology engineering environment by means of the K-means clustering algorithm only by looking at the type of interactions a user makes. In this paper we use the GOSPL ontology engineering tool and method to demonstrate this method. The data used to demonstrate the method stems from two ontology engineering projects involving respectively 42 and 36 users.

1. 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

2. 22-9-2015 pag. 2
Overview
• Motivation
• User profiling
• … definition
• … in the research field
• Ontology engineering
• Method
• Use Case
• Conclusions and Future work

3. 22-9-2015 pag. 3
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 and
reliability in user
Roles and Responsibilities
vs
Users

4. 22-9-2015 pag. 4
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)

5. 22-9-2015 pag. 5
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

6. 22-9-2015 pag. 6
User profile
• Behaviour based user profiling
• Behavioural dimensions
• Focus dispersion
• Engagement
• Contribution
• Initiation
• Content Quality
• Popularity
“How to determine user role/profile based
on the type of input of a user?”

7. 22-9-2015 pag. 7
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

8. 22-9-2015 pag. 8
Ontology Engineering
• GOSPL

9. 22-9-2015 pag. 9
Ontology Engineering
• Interactions in GOSPL tool
•Acting like forum
•Difference between forum and O.E.:
• Closer
• Goal-oriented
• Deadline driven

10. 22-9-2015 pag. 10
Method

11. 22-9-2015 pag. 11
Method – Extraction Phase
• Apply D2RQ mapping
of GOSPL ontology
Social
interaction
(sioc:Item)
Vote Sioc:Post Reply …
Gloss
interactions
Gloss
interactions
…
ADD
gloss
UPDATE
gloss
DELETE
gloss
…
Social
interaction
(sioc:Item)
Vote Sioc:Post Reply …
Gloss
interactions
Gloss
interactions
…
ADD
gloss
UPDATE
gloss
DELETE
gloss
…
Social interaction
(sioc:Item)
Vote sioc:Post Reply …
Gloss
interactions
Lexon
interactions
…
ADD
gloss
UPDATE
gloss
DELETE
gloss
…ADD
gloss
UPDATE
gloss
DELETE
gloss

12. 22-9-2015 pag. 12
Method – Manipulation Phase
• Standardize dataset
• Principal Component Analysis (PCA)
• Transformation of variables (ortogonal)
• Reduce dimensionality
• Compose new matrix

13. 22-9-2015 pag. 13
Method – Clustering Phase
• K-means clustering
• ANOVA
• Silhouette coefficients
• Take best result => different profiles

14. 22-9-2015 pag. 14
Use Case
1
2
3
4
5
6
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8
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10
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42
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1
36
42
22
30
39
41
2
15
25
11
4
16
17
18
10
26
27
14
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13
12
19
38
21
28
6
37
7
3
5
33
32
31
8
34
35
24
23
40
29
Work in teams

15. 22-9-2015 pag. 15
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

16. 22-9-2015 pag. 16
Use Case
• Standardize data (z-score)
• PCA transformations
• 95% of variance
• Iterative process
• Original: 42 users 10 dimensions
After PCA: 42 users 05 dimensions

17. 22-9-2015 pag. 17
Use Case
• K-mean clustering
• Silhouette calculations
• ANOVA testing
• α = 0.95

18. 22-9-2015 pag. 18
Use Case

19. 22-9-2015 pag. 19
Use Case
9
1
36
42
22
30
39
41
2
15
25
11
4
16
17
18
10
26
27
14
20
13
12
19
38
21
28
6
37
7
3
5
33
32
31
8
34
35
24
23
40
29
9
1
36
42
22
30
39
41
2
15
25
11
4
16
17
18
10
26
27
14
20
13
12
19
38
21
28
6
37
7
3
5
33
32
31
8
34
35
24
23
40
29
Cluster 1
Cluster 4
Cluster 2
Cluster 3
Cluster 5

20. 22-9-2015 pag. 20
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

21. 22-9-2015 pag. 21
Conclusions and Future Work
• Discussion & future work
• Sensitive to active and passive users
• Combine with classic behavioural
dimensions
• Validation cluster quality
• Dunn index
• Davies-Bouldin index
• C-index
• Iterate process and re-evaluate

22. 22-9-2015 pag. 22
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.

23. 22-9-2015 pag. 23
THANK YOU!