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ONTOLOGY MAPPING FOR LIFE SCIENCE LINKED DATA
Amrapali Zaveri and Michel Dumontier
Stanford Center for Biomedical Informatics ResearchStanford University
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Large and growing network of Linked Data
ISWC2016:::BMDID::DumontierLinking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/"
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Linked Data for the Life Sciences
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Bio2RDF is an open source project to unify the representation and interlinking of biological data using RDF.
chemicals/drugs/formulations, genomes/genes/proteins, domainsInteractions, complexes & pathwaysanimal models and phenotypesDisease, genetic markers, treatmentsTerminologies & 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
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Biomedical Linked Data
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the lack of coordination to a global schema makes Linked Data chaotic and unwieldy
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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> . }}
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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
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.
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Semanticscience Ontology (SIO)An effective upper level ontology.1500+ classes207 object properties (inc. inverses)1 datatype property
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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, ?procFROM <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 .
}}
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Many vocabularies, ontologies and community-based standards
are now available
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PubChem uses multiple terminologies
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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
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Goal
Develop a semi-automated procedure to generate high quality mappings between Bio2RDF and SIO.
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approach
distance metrics
graph-based
instance-based
BioPortal
crowdsourcing
previous work*Our work
Automated Manual
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Idea: Create mappings between SIO and Bio2RDF using ontologies in BioPortal
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Bio2RDF
NCBO Annotator/Recommender
SIO
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Bio2RDF-SIO mappings via transitive closure through BioPortal ontologies
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Bio2RDF
SIO
Super Class
Mapped Class
match
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Results
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319 (of 6093) classespruned
1 NCBO Annotator 174 Bio2RDF classesmatched directly and exactly to SIO
2 NCBO Recommender94 Bio2RDF classes matched toBioPortal ontologies
Bio2RDFremove blank nodes, general resources, OWL vocabulary & non-Bio2RDF types/relations.
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Results
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SIO1500 classes
475 BioPortalOntologies3
393 BioPortal ontologiesmatched to SIO
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Results
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Bio2RDF319 classes
4 Traverse hierarchySIO1500 classes
393 BioPortal ontologiesmatched to SIO
94 Bio2RDF classes matched toBioPortal ontologies
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Results
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Bio2RDF319 classes
4 Traverse hierarchy
SIO1500 classes
393 BioPortal ontologiesmatched to SIO
94 Bio2RDF classes matched toBioPortal ontologies
71 matches
Mapped class
Super class
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Results — Example
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Bio2RDFclass
clinicaltrials:Clincial-Study
Super class
Edda:Study_Design
Mapped class
edda:clinical_trial
SIOclass
sio:001041| (study design)
skos:broader
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Mappings often occurred to more than one class
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sider:Drug-Indication-Association
sio:010038 (drug)
sio:010299 (disease)
sio:000897 (association)
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Manual validation of mappings
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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
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Conclusion
• Developed a semi-automated methodology to map Bio2RDF classes to SIO via BioPortal ontologies
• 245 of 319 Bio2RDF classes matched to SIO
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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
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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
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dumontierlab.commichel.dumontier@stanford.edu
Website: http://dumontierlab.com
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