1. A Family‐Based Framework for Supporting
Quality Assurance of Biomedical
Ontologies in BioPortal
Zhe He (Henry)1,
Christopher Ochs1, Ankur Agrawal2, Yehoshua Perl1, Dimitrios
Zeginis3, Konstantinos Tarabanis3, Gai Elhanan4,
Michael Halper1, Natasha Noy5, and James Geller1
1New Jersey Institute of Technology,
2Manhattan College, 3University of Macedonia,
4Halfpenny Technologies, 5Stanford University
1

2. Outline
• Quality Assurance (QA) of biomedical ontologies
• Abstraction Networks to support QA
• How to perform QA for hundreds of ontologies
• Family‐based QA framework & family categorization
• QA Illustration of Cancer Chemoprevention Ontology
2

3. Biomedical Ontologies
• Biomedical ontologies consist of specifications of the
entities, attributes, and relationships between the
entities.
• Importance in Electronic Health Record systems,
clinical decision support systems, and biomedical
research.
• Many well‐known biomedical ontologies and
terminologies are using OWL standard, e.g. NCIt,
Gene Ontology, etc.
3

4. Visualization of CanCo (127 Classes)
4

5. Abstraction Networks
• An Abstraction Network is a secondary network that
provides a compact view of the structure and content of
the primary ontology.
Ontology
Abstraction
Network
Subset of classes
modeled by a node
5

6. Area Taxonomy
Area: Set of all classes that have the same set of object properties.
6

7. Partial Area Taxonomy
Root: Class with no superclasses in area
Partial area: Root + all descendants in area
7

8. QA Based on Partial Area Taxonomy
• Summarizes sets of structurally and semantically similar
classes for Description‐Logic‐based terminologies.
• Derived in a specific tailored way to support QA for
– NCIt (Min et al. 2006), SNOMED CT (Wang et al. 2007) ,
OCRe (Ochs et al. 2012), SDO (Ochs et al. 2013), etc.
• Anomalies in Abstraction Network help to identify
inconsistencies, missing information, and modeling errors,
etc.
8

9. Partial Area
Taxonomy
for CanCo 0.2
9

10. BioPortal
• A Web‐based repository of biomedical ontologies
provided by the National Center for Biomedical Ontology
(NCBO) at Stanford (Musen et al. 2012)
• Contains about 5.9 million classes from 357 biomedical
ontologies
– Web Ontology Language (OWL)
– Open Biomedical Ontologies (OBO)
– UMLS Rich Release Format (RRF)
10

11. Quality Assurance of Ontologies in BioPortal
• The content of BioPortal is growing in importance.
– Domain: Drug, anatomy, gene, disease and symptom, etc.
• Support biomedical research and applications.
• Interdisciplinary research project depends on the ontology’s
accuracy.
• The quality of ontologies in BioPortal varies due to
heterogeneous development models, domain knowledge of
curators, etc.
• BioPortal would benefit from the inclusion of a QA framework.
11

12. Difficulties with QA of many Ontologies
• Design Abstraction Networks for supporting QA for more than
350 ontologies in BioPortal
• It can be tedious research work deriving new kinds of
Abstraction Networks for each new ontology encountered.
AERO
ATMO AAO
APO
BAO
BRO
FBbi
BNO
BHO
CCO CTX
CMO
CPO
CNO
CPTH
CPTAC
FB‐BT
EDDA
GALEN
………
….......
12

13. Family‐Based QA Framework
• Try to discover common underlying structure of many
ontologies
• Ontologies may be grouped according to certain combinations
of structural features.
• Define families based on various combinations of structural
features
• Unified Abstraction Network and QA methodologies for each
family
• A software tool will have a module for each family.
13

14. Structural Features of OWL‐based Ontologies
14
• Object properties: relate classes and represent potential
relationships between class instances
• Given explicitly defined domain and range (domain defined)
• Used in class restriction, less strict than domain defined
(restriction defined)
• Data properties
Disease has_disease_location Organ
has_measurement_value floatScalar measurement
datum

15. Structural Features of OWL‐based Ontologies
Tree structure (no class has multiple parents)
Directed Acyclic Graph (some class has multiple parents)
15

16. Sample Set of BioPortal Ontologies
• 210 distinct BioPortal ontologies were analyzed. (210 / 357 =
59%)
• Ontologies in OWL or OBO format were considered.
• HermiT (Shearer et al. 2008) to classify all 210 ontologies in
asserted view to inferred view.
• 24 of those 210 ontologies could not be classified due to
various reasons. (For example, inconsistency with OWL)
• Final sample set consisted of 186 ontologies
16

17. Commonality of Structural Features
Characteristic # Ontologies
w/Characteristic
% of Sample (n = 186)
Object properties 150 80.6
Domains‐defined object
properties
81 43.5
Restriction‐defined object
properties
131 70.4
Data properties 71 38.2
Multiple parents (DAG) 110 59.1
No multiple parents (Tree) 76 40.9
17

18. Seven Families of Ontologies
Family Structural Condition # Ontologies Samples
1 All object properties are
instantiated
2 SNOMED CT, NCIt
2 With only domain‐defined object
properties
19 CanCo, ICF, PMR
3 With only restriction‐defined
object properties
69 GO, GRO_CPD, HPIO
4 With either domain‐defined
object properties or restriction‐
defined object properties
62 SDO, IDO
5 DAG, no object properties 9 APO, HP, OGMD
6 Tree, no object properties, with
data properties
3 CBO, CareLex
7 Tree, no object properties,
without data properties
22 OGMS, REPO, SEP
18

19. Unified Quality Assurance Methods
• Unified QA methodologies for a whole family
• BLUOWL (Biomedical Layout Utility for OWL‐Based
Ontologies) developed by Ochs (forthcoming)
automatically generates partial area taxonomy for
ontologies with object properties.
• Quality Assurance based on abstraction networks
generated by BLUOWL tool
– Detecting inconsistencies, modeling errors, etc.
19

20. QA Illustration for the CanCo in Family 2
• Cancer Chemoprevention Ontology
• Curator: Dimitrios Zeginis
• Developed to formally define the fundamental entities
used for annotating and describing cancer
chemoprevention related data
• In Version 0.2, CanCo has 127 classes and 37 object
properties.
20

21. Partial Area
Taxonomy
for CanCo 0.2
21

22. Errors Found in Canco Taxonomy
Error Suggestions Outcome
max_inhibitory_
concentration is
identical to an object
property
Remove the class
max_inhibitory_concentr
ation
Change made
in Version 0.3
Class name of
“Target” is not
appropriate
“Target”
“BiologicalTarget”
Change made
in Version 0.3
Redundant BFO
classes in Entity node
20 BFO classes should be
hidden
Future plan of
BioPortal for
hiding classes
22

23. Partial Area
Taxonomy
for CanCo 0.3
23

24. Outstanding Partial Areas Contains More Errors
• For SNOMED CT and NCIt, small partial area were
shown to have high likelihood of errors (Wang et al.
2007) (Min et al. 2006)
• For OCRe, SDO and CanCo, large partial areas were
shown to indicate higher concentration of errors.
(Ochs et al. 2012) (Ochs et al. 2013) (He et al. 2013)
24

25. Abstraction Networks Derived for Different Families
• Family 1 (All object properties are instantiated)
– SNOMED CT (Wang et al. 2007)
– NCIt (Min et al. 2006)
• Family 2 (with only domain defined object properties)
– OCRe (Ochs et al. 2012)
– CanCo (He et al. 2013)
• Family 3 (with only restriction defined object properties)
– GO – Biological Process (SABOC Website)
• Family 4 (with domain and restriction defined object properties)
– DDI (He et al. 2013)
– SDO (Ochs et al. 2013)
25

26. Future Work
• Abstraction Networks for families with no object
properties or data properties
• An ontology may exhibit several structural features.
• If one Abstraction Network does not work well for QA,
another one may.
– E.g. an ontology with object properties and data
properties
• More generalized way to define families
26

27. Conclusions
• Analyzed 186 BioPortal ontologies for preliminary results.
• Identified several structural features that enabled the
classification of the ontologies into families
• Abstraction Network for the whole family
• A preliminary QA review of CanCo
• A uniform QA methodology design for each family will
achieve improved efficiency
– Critical with the limited QA resources available too most curators
27

28. References (1)
• Wang Y, Halper M, Min H, Perl Y, Chen Y, Spackman KA. Structural
methodologies for auditing SNOMED. J Biomed Inform. 2007 Oct;40(5):561‐81.
• Min H, Perl Y, Chen Y, Halper M, Geller J, Wang Y. Auditing as part of the
terminology design life cycle. J Am Med Inform Assoc. 2006;13(6):676‐90.
• Ochs C, Agrawal A, Perl Y, et al. Deriving an abstraction network to support
quality assurance in OCRe. AMIA Annu Symp Proc. 2012
• Ochs C, He Z, Perl Y, Arabandi S, Halper M, Geller J. Refining the Granularity of
Abstraction Networks for the Sleep Domain Ontology. In Proc of the 4th
International Conference on Biomedical Ontology. Montreal, QC, Canada; 2013.
• Musen MA, Noy NF, Shah NH, Whetzel PL, Chute CG, et al. The National Center
for Biomedical Ontology. J Am Med Inform Assoc, 2012;19(2): 190‐5.
• Geller J, Ochs C, Perl Y, Xu J, New Abstraction Networks and a New Visualization
Tool in Support of Auditing the SNOMED CT Content , AMIA Annul Symp Proc
2013.
28

29. References (2)
• He Z, Ochs C, Soldatova L, Perl Y, Arabandi S and Geller J, Auditing
Redundant Import in Reuse of a Top Level Ontology for the Drug Discovery
Investigations Ontology. In Proc of the 2013 International Workshop on
Vaccine and Drug Ontology Studies, July 7, 2013. Montreal, Qc, Canada.
• Zeginis D, Hasnain A, Loutas N, Deus HF, Fox R, Tarabanis K, A
collaborative methodology for developing a semantic model for
interlinking Cancer Chemoprevention linked data sources. Semantic Web
Journal, 2013.
• Shearer R, Motik B, Horrocks I. HermiT: a highly‐efficient OWL reasoner.
Proceedings of the 5th International Workshop on OWL: Experiences and
Directions (OWLED 2008); 2008
29

30. Thank you !
Any Questions ?
30
Contact
• Zhe He (Henry)
• PhD Candidate of Computer Science
• Email: zhe.he@njit.edu

31. Supplements
31

32. Unused Classes from Top‐Level Ontology
• In the root partial area Entity (49), 39 out of 49 classes were
migrated from BFO, which is modeled without object
properties. Of them 20 are leaves (without children) in CanCo.
• Discovery: These 20 classes are not needed!
• Suggestion: Should be hidden (He et al. 2013)
32

33. Found problem: Class “max_inhibitory_concentration”
identical to the object property “max_inhibitory_conc”
33
The anomalies found in the CanCo taxonomy helped to detect
problems in CanCo’s modeling.

34. Changes in CanCo Version 0.3
• CanCo curators deleted the class “max_inhibitory_concentration”.
CanCo Version 0.2 CanCo Version 0.3
34

35. Renaming of “Target”
• Another “large” partial area Molecule (7)
• The child of Molecule – Target should be renamed “Biological
target” according to its definition.
• A class Macromolecule should be introduced as child of Molecule.
CanCo
Version 0.2
35

36. Changes in CanCo Version 0.3
• The curators of CanCo have implemented the changes in version 0.3.
CanCo 0.2 CanCo 0.3 36

37. 37

38. Preliminary Structural Meta‐Ontology for BioPortal
38