BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.

1. BiOnIC: A Catalog of User Interactions
with Biomedical Ontologies
16th Interna+onal Seman+c Web Conference (ISWC)
Vienna, 21st - 25th October 2017
M A U L I K KA M D A R , S I M O N WA L K , TA N I A T U D O R A C H E , MA R K MU S E N
Stanford Center for Biomedical Informa:cs Research
maulikrk@stanford.edu

2. Benefits of analyzing user interac+ons
Ø Ontology Engineers:
v Iden+fy explora+on and querying paVerns
v Understand ontology usage and reuse
v Prune unwanted classes and rela+ons
Ø Ontology Repository Maintainers:
v Categorize user behaviors
v Develop intelligent interfaces
v Provide targeted recommenda+ons
Ø Biomedical Researchers:
v Iden+fy temporal research trends
v Iden+fy frequently accessed classes

3. BiOnIC: A Catalog of User Interac+ons with
Biomedical Ontologies
hVp://onto-apps.stanford.edu/bionic/datasets

4. hVp://bioportal.bioontology.org/

5. hVp://bioportal.bioontology.org/

6. hVp://bioportal.bioontology.org/

7. hVp://bioportal.bioontology.org/

8. hVp://bioportal.bioontology.org/

9. NCBO API Usage
APIRequestsperMonth
2013−O
ct
2014−Jan
2014−Apr
2014−Jul
2014−O
ct
2015−Jan
2015−Apr
2015−Jul
2015−O
ct
2016−Jan
2016−Apr
2016−Jul
2016−O
ct
2M8M32M
Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
NCBO Website Traffic
OccurrencesperMonth
2009−Jan
2010−Jan
2011−Jan
2012−Jan
2013−Jan
2014−Jan
2015−Jan
2016−Jan
0100K200K
Page Requests
Unique IP Addresses
BiOnIC datasets crea+on
• Removing robot/invalid
requests
• Normalizing ontology
iden+fiers and class IRIs

10. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
• January 2015 version.
• Ontologies should have
classes that are reused by
others OR reuse classes
from other ontologies.
• Ontologies should have
minimum of 10 unique
users via WebUI and API

11. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
Class Sta:s:cs Datasets
For each class in each ontology:
• Access AGributes:
o Total IP Requests (WebUI/API)
o Unique IP Requests (WebUI/API)
• Reuse AGributes:
o Number of ontologies reusing a class
• Structural AGributes:
o Number of parent/child/sibling classes
o Depth from ontology root

12. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

13. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

14. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

15. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

16. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

17. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

18. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

19. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

20. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

21. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2
Class Depth ->
2a
1
3a 4a
4b
4c 2b
3b
3c
1’
2a’
2b’
2c’
3a’
3b’
3c’

22. 2a’ 1’ 2b’ 3b’
2a 3a 4a 3a 1
Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
User Interac:on Sequences Datasets
Ontology 1
Ontology 2

23. Filtering
Access Logs
Filtering
Ontologies
Compu+ng
Class Counts
Compu+ng
Sequences
Anonymizing
Data
BiOnIC datasets crea+on
Anonymiza:on Steps
• IP addresses anonymized using unique SHA-224 hash-encoded
user iden+fiers generated from “user_<Random
String>_<Random_Integer>”.
• e.g. 39fd4e6d569a034973g61bb392a694d4eabe1ef98c43ee68ca2fc86
• Absolute Time-stamps converted to rela+ve +me-stamps, with
respect to first interac+on with BioPortal repository.
• e.g. 0, 2757, 2786, 3586, 3618, 3803, 3959, 4047, 5111 (s), …

24. BiOnIC schema to model sta+s+cs and sequences data
countStat
bionic:CountStat
bionic:ReuseCount
- reuseType
- reusingOntologies
bionic:RequestCount
- accessType
- year
- totalUsers
- uniqueUsers
prov:Agent bionic:Sequence
- accessType
- totalTime
- uniqueClasses
bionic:SeqEn:ty
- rela6veTimestamp
bionic:Ontology
- skos:prefLabel
- totalClasses
- maxDepth
owl:Class
- skos:prefLabel
skos:Collec:on skos:Concept
begin
end
nextEn6ty
class
class
requests
skos:member
bionic:SeqDataset
- accessType
bionic:StatDataset dcat:Dataset
sequence
classInfo
ontology
ontology
bionic:ClassInfo
- siblings
- directParents
- directChildren
- classDepth
class
subClassOf
ontology
SKOS, PROV and DCAT standards are reused in the BiOnIC schema.

25. hVp://onto-apps.stanford.edu/bionic/datasets
BiOnIC datasets

26. hVp://onto-apps.stanford.edu/bionic/datasets
BiOnIC datasets
hVp://www.rdjdt.org/

27. hVp://onto-apps.stanford.edu/bionic/datasets
BiOnIC datasets
hVp://www.rdjdt.org/
SPARQL
Triplestore /
Triple PaGern
Fragment Server

28. hVp://onto-apps.stanford.edu/bionic/datasets
BiOnIC datasets
hVp://www.rdjdt.org/
SPARQL
Triplestore /
Triple PaGern
Fragment Server
BioPortal
SPARQL Endpoint

29. Characteris+cs of the BiOnIC Catalog
• WebUI Access: 5.4M class requests, 1M unique agents
• API Access: 67.2M class requests, 205K unique agents
• 255 biomedical ontologies

30. VisIOn (Visualizing Ontology Interac+ons) Web Applica+on
hVp://onto-apps.stanford.edu/vision

31. VisIOn (Visualizing Ontology Interac+ons) Web Applica+on
hVp://onto-apps.stanford.edu/vision

32. VisIOn (Visualizing Ontology Interac+ons) Web Applica+on
hVp://onto-apps.stanford.edu/vision

33. Applica+ons of BiOnIC and VisIOn

34. Temporal influences in browsing and querying
Fisher’s exact test with FDR: Certain classes (e.g. Ebolavirus) or sets of classes are browsed
or queried significantly more, when compared between different +me periods.
2016
2015

35. Interface influences in browsing and querying
Number of Unique API Users (Log Scale)
Number of Unique WebUI Users (Log Scale)
1000
10
100
10 100 1000
1
1
Certain classes browsed or
queried significantly more.

36. Interface influences in browsing and querying
Number of Unique API Users (Log Scale)
Number of Unique WebUI Users (Log Scale)
1000
10
100
10 100 1000
Female
Reproduc:ve
System
1
1
Certain classes browsed or
queried significantly more.
Dermis

37. Interface influences in browsing and querying
Dysmorphic
Syndrome
Night
blindness
Number of Unique API Users (Log Scale)
Number of Unique WebUI Users (Log Scale)
1000
10
100
10 100 1000
Female
Reproduc:ve
System
1
1
Certain classes browsed or
queried significantly more.
Dermis

38. Explora+on and Querying behavioral paVerns
• Certain classes in the lower levels of the ontological hierarchy are rarely browsed and
queried – this may be an ar+fact of the indented tree visualiza+on.
• More triangular polygons (1 parent -> 2 children classes, or 2 parents -> 1 child class)
observed in WebUI Access polygon due to indented tree visualiza+on.

39. Modeling user behaviors through Markov Chains
Walk, et al. How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs. WWW 17

40. Novel research direc+ons may be enabled
through the BiOnIC and VisIOn resources
• Categorize user browsing behaviors by incorpora+ng the
structural features of the ontology classes.
• Develop personalized user interfaces for ontology naviga+on,
which take into account the user type and the predic+ons of
the next class that a user is likely to access.
• Develop advanced methods for ontology summariza+on and
modulariza+on, using BiOnIC datasets as features.
…

43. Acknowledgments
Musen Lab, Stanford
BMI PhD Program, Stanford
US NIH Grants
U54-HG004028
GM086587
maulikrk@stanford.edu
hVp://onto-apps.stanford.edu/bionic
hVp://onto-apps.stanford.edu/vision