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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
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
BiOnIC:	A	Catalog	of	User	Interac+ons	with	
Biomedical	Ontologies	
hVp://onto-apps.stanford.edu/bionic/datasets
hVp://bioportal.bioontology.org/
hVp://bioportal.bioontology.org/
hVp://bioportal.bioontology.org/
hVp://bioportal.bioontology.org/
hVp://bioportal.bioontology.org/
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
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
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
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’
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’
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’
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’
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’
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’
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’
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’
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’
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’
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
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),	…
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.
hVp://onto-apps.stanford.edu/bionic/datasets		
BiOnIC	datasets
hVp://onto-apps.stanford.edu/bionic/datasets		
BiOnIC	datasets	
hVp://www.rdjdt.org/
hVp://onto-apps.stanford.edu/bionic/datasets		
BiOnIC	datasets	
hVp://www.rdjdt.org/		
SPARQL	
Triplestore	/		
Triple	PaGern		
Fragment	Server
hVp://onto-apps.stanford.edu/bionic/datasets		
BiOnIC	datasets	
hVp://www.rdjdt.org/		
SPARQL	
Triplestore	/		
Triple	PaGern		
Fragment	Server	
BioPortal	
SPARQL	Endpoint
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
VisIOn	(Visualizing	Ontology	Interac+ons)	Web	Applica+on	
hVp://onto-apps.stanford.edu/vision
VisIOn	(Visualizing	Ontology	Interac+ons)	Web	Applica+on	
hVp://onto-apps.stanford.edu/vision
VisIOn	(Visualizing	Ontology	Interac+ons)	Web	Applica+on	
hVp://onto-apps.stanford.edu/vision
Applica+ons	of	BiOnIC	and	VisIOn
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
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.
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
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
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.
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
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.		
…
BiOnIC: A Catalog of User Interactions with Biomedical Ontologies
BiOnIC: A Catalog of User Interactions with Biomedical Ontologies
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

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BiOnIC: A Catalog of User Interactions with Biomedical Ontologies