Bio2RDF is an open-source project that offers a large and connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in the ontology will offer a better-integrated view of the Life Science Linked Data

1. ONTOLOGY MAPPING
FOR LIFE SCIENCE LINKED DATA
ISWC2016:::BMDID::Dumontier1
Amrapali Zaveri and Michel Dumontier
Stanford Center for Biomedical Informatics Research
Stanford University

2. Large and growing network of Linked Data
2 ISWC2016:::BMDID::DumontierLinking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/"

3. ISWC2016:::BMDID::Dumontier
Linked Data for the Life Sciences
3
Bio2RDF is an open source project to unify the
representation and interlinking of biological data using RDF.
chemicals/drugs/formulations,
genomes/genes/proteins, domains
Interactions, complexes & pathways
animal models and phenotypes
Disease, genetic markers, treatments
Terminologies & publications
• 11B+ interlinked statements from 35 biomedical
datasets and 400+ ontologies
• dataset description, provenance & statistics
• A growing interoperable ecosystem with the EBI,
NCBI, DBCLS, NCBO, OpenPHACTS, and
commercial tool providers

4. Biomedical Linked Data
ISWC2016:::BMDID::Dumontier4

5. the lack of coordination to a global schema
makes Linked Data chaotic and unwieldy
ISWC2016:::BMDID::Dumontier5

6. Federated queries require intimate
knowledge of each dataset schema
Get all protein catabolic processes (and more specific GO terms) in biomodels
SELECT ?go ?label count(distinct ?x)
WHERE {
service <http://bioportal.bio2rdf.org/sparql> {
?go rdfs:label ?label .
?go rdfs:subClassOf+ ?tgo
?tgo rdfs:label ?tlabel .
FILTER regex(?tlabel, "^protein catabolic process")
}
service <http://biomodels.bio2rdf.org/sparql> {
?x <http://bio2rdf.org/biopax_vocabulary:identical-to> ?go .
?x a <http://www.biopax.org/release/biopax-level3.owl#BiochemicalReaction> .
}
}
ISWC2016:::BMDID::Dumontier6

7. uniprot:P05067
uniprot:Protein
is a
sio:gene
is a is a
Previous work involved manual mappings between
Bio2RDF types and relations and the Semanticscience
Integrated Ontology (SIO)
dataset
ontology
Knowledge Base
ISWC2016:::BMDID::Dumontier
pharmgkb:PA30917
refseq:Protein
is a
is a
omim:189931
omim:Gene pharmgkb:Gene
Querying Bio2RDF Linked Open Data with a Global Schema. Alison Callahan, José Cruz-Toledo and
Michel Dumontier. Bio-ontologies 2012.
7

8. ISWC2016:::BMDID::Dumontier8
Semanticscience Ontology (SIO)
An effective upper level ontology.
1500+ classes
207 object properties (inc. inverses)
1 datatype property

9. Bio2RDF and SIO powered SPARQL federated query:
Find chemicals (from CTD) and proteins (from SGD) that
participate in the same process (from GOA)
SELECT ?chem, ?prot, ?proc
FROM <http://bio2rdf.org/ctd>
WHERE {
SERVICE <http://ctd.bio2rdf.org/sparql> {
?chemical a sio:chemical-entity.
?chemical rdfs:label ?chem.
?chemical sio:is-participant-in ?process.
?process rdfs:label ?proc.
FILTER regex (?process, "http://bio2rdf.org/go:")
}
SERVICE <http://sgd.bio2rdf.org/sparql> {
?protein a sio:protein .
?protein sio:is-participant-in ?process.
?protein rdfs:label ?prot .
}
} ISWC2016:::BMDID::Dumontier9

10. Many vocabularies, ontologies
and community-based standards
are now available
ISWC2016:::BMDID::Dumontier

11. PubChem uses multiple terminologies
ISWC2016:::BMDID::Dumontier11

12. Existing limitations
with Bio2RDF mappings
• New datasets have been added
• Existing datasets have changed
• The target ontology (SIO) has changed
• The target ontology (SIO) is incomplete and there
may be better ontologies to use
• These ontologies are evolving, today’s mappings
may be invalid or imprecise tomorrow
• Manual process -> not easy and not reproducible
-> must automate
ISWC2016:::BMDID::Dumontier12

13. Goal
Develop a semi-automated procedure to
generate high quality mappings between
Bio2RDF and SIO.
ISWC2016:::BMDID::Dumontier13

14. approach
14
distance
metrics
graph
-based
instance
-based
BioPortal
crowdsourcing
previous work*
Our work
Automated Manual
ISWC2016:::BMDID::Dumontier

15. Idea: Create mappings between SIO and
Bio2RDF using ontologies in BioPortal
15
Bio2RDF
NCBO Annotator/
Recommender
SIO
ISWC2016:::BMDID::Dumontier

16. Bio2RDF-SIO mappings via transitive
closure through BioPortal ontologies
16
Bio2RDF
SIO
Super Class
Mapped Class
match
ISWC2016:::BMDID::Dumontier

17. Results
17
319 (of 6093) classes
pruned
1 NCBO Annotator
174 Bio2RDF classes
matched directly
and exactly to SIO
2 NCBO Recommender
94 Bio2RDF classes
matched to
BioPortal ontologies
Bio2RDF
remove blank nodes, general resources, OWL
vocabulary & non-Bio2RDF types/relations.
ISWC2016:::BMDID::Dumontier

18. Results
18
SIO
1500 classes
475 BioPortal
Ontologies
3
393 BioPortal
ontologies
matched to SIO
ISWC2016:::BMDID::Dumontier

19. Results
19
Bio2RDF
319 classes
4 Traverse hierarchy
SIO
1500 classes
393 BioPortal
ontologies
matched to SIO
94 Bio2RDF classes
matched to
BioPortal ontologies
ISWC2016:::BMDID::Dumontier

20. Results
20
Bio2RDF
319 classes
4 Traverse hierarchy
SIO
1500 classes
393 BioPortal
ontologies
matched to SIO
94 Bio2RDF classes
matched to
BioPortal ontologies
71 matches
Mapped class
Super class
ISWC2016:::BMDID::Dumontier

21. Results — Example
21
Bio2RDF
class
clinicaltrials:Clincial-Study
Super class
Edda:Study_Design
Mapped class
edda:clinical_trial
SIO
class
sio:001041| (study design)
skos:broader
ISWC2016:::BMDID::Dumontier

22. Mappings often occurred
to more than one class
22
sider:Drug-Indication-Association
sio:010038 (drug)
sio:010299 (disease)
sio:000897 (association)
ISWC2016:::BMDID::Dumontier

23. Manual validation of mappings
23
Bio2RDF Class SIO Class Annotation
drugbank:Biotech no match
clinicaltrials:Organization sio:00012 (organization) exact
drugbank:toxicity sio:001008 (toxicity) exact
sgd:GlycineCount sio:000794 (count) partial – is-a
wormbase:Genetic-
Interaction
sio:010035 (gene) partial – part-of
clinicaltrials:Serious-Event sio:000614 (attribute) incorrect
drugbank:Source sio:000510 (model) incorrect
All results available at https://goo.gl/eiijmQ ISWC2016:::BMDID::Dumontier

24. Conclusion
• Developed a semi-automated
methodology to map Bio2RDF classes to
SIO via BioPortal ontologies
• 245 of 319 Bio2RDF classes matched to
SIO
24 ISWC2016:::BMDID::Dumontier

25. Limitations
• Unmatched classes: neither SIO nor other
ontologies have complete coverage
• Overly general concepts: Semantically
incompatible classes
• Incorrect mappings: Matches to part of the
class
• Mappings are insufficient to precisely to
retrieve data across different datasets
25 ISWC2016:::BMDID::Dumontier

26. Future Work
• Extend SIO to include classes that are
ultimately not found
• Explore mid-level portion of SIO to eliminate
root level mappings
• Scalable validation by via crowdsourcing
• Pursue query rewriting
26 ISWC2016:::BMDID::Dumontier

27. dumontierlab.com
michel.dumontier@stanford.edu
Website: http://dumontierlab.com
27 ISWC2016:::BMDID::Dumontier