Tactical Formalization of Linked Open Data (Ontology Summit 2014)

Tactical Formalization of Linked Open Data
@micheldumontier::OntologySummit2014:March 6,20141
Michel Dumontier, Ph.D.
Associate Professor of Medicine (Biomedical Informatics)
Stanford University

Linked Open Data provides an incredibly dynamic, rapidly
growing set of interlinked resources

Linked Data for the Life Sciences
3
Bio2RDF converts bio-data in RDF format and ensures
URI integrity by conferring with its registry of datasets

the simplicity of the triple makes it
easy to proliferate

but the lack of coordination makes Linked
Open Data is quite chaotic and unwieldy

Massive Proliferation of Ontologies / Vocabularies

Despite all the data, it’s still hard to find answers to questions
Because there are many ways to represent the same data
and each dataset represents it differently

uniprot:P05067
uniprot:Protein
is a
sio:gene
is a is a
Semantic data integration, consistency checking and
query answering over Bio2RDF with 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.
8

@micheldumontier::OntologySummit2014:March
9
SRIQ(D)
10700+ axioms
1300+ classes
201 object properties (inc. inverses)
1 datatype property

Bio2RDF and SIO powered SPARQL 1.1 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 {
?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 .
}
}

multiple formalizations of the same kind of
data has emerged, each with their own merit

Multi-Stakeholder Efforts to Standardize
Representations are Reasonable,
Long Term Strategies for Data Integration

tactical formalization
discovery of drug and disease pathway associations
Take what you need
and represent it in a way that directly serves your objective

Biological Pathways Define A
Biological Objective
def: A biological pathway is constituted by a
set of molecular components that undertake
some biological transformation to achieve a
stated objective
glycolysis : a pathway that converts glucose to
pyruvate

aberrant and pharmacological pathways
Q1. Can we identify pathways that are associated with a
particular disease or class of diseases?
Q2. Can we identify pathways are associated with a
particular drug or class of drugs?
drug
pathway
disease

Identification of
drug and disease enriched pathways
• Approach
– Integrate 3 datasets
• DrugBank, PharmGKB and CTD
– Integrate 7 terminologies
• MeSH, ATC, ChEBI, UMLS, SNOMED, ICD, DO
– Formalize
– Identify significant associations using enrichment
analysis over the fully inferred knowledge base
Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.
Bioinformatics. 2012. @micheldumontier::OntologySummit2014:March 6,201416

Have you heard of OWL?

RDF triples are underspecified bits of
knowledge. OWL can help you nail
down what was really meant
Natural language statements:
The nucleus is a key part of the cell.
RDF triple:
<nucleus> <part-of> <cell>
OWL
• Nucleus and Cell are classes
• part-of is a relation between 2 instances
• Formalization: every instance of Nucleus ‘is part of’ at least
one instance of Cell
OWL axiom:
Nucleus subClassOf part-of some Cell 18@micheldumontier::OntologySummit2014:March 6,201418

assigning meaning to triples:
domain expertise + logics required!
Convert RDF triples into OWL axioms.
Triple in RDF:
<C1 R C2>
• C1 and C2 are classes, R a relation between 2 classes
• intended meaning:
o C1 SubClassOf: C2
o C1 SubClassOf: R some C2
o C1 SubClassOf: R only C2
o C2 SubClassOf: R some C1
o C1 SubClassOf: S some C2
o C1 SubClassOf: R some (S only C2)
o C1 DisjointFrom: C2
o C1 and C2 SubClassOf: owl:Nothing
o R some C1 DisjointFrom: R some C2
o C1 EquivalentClasses C2
o ...
• in general: P(C1, C2), where P is an OWL axiom (template)
Challenge:
Formalizing data
requires one to commit
to a particular meaning
– to make an ontological
commitment

Axiom Patterns for Triples
<nucleus> <part-of> <cell>
?X part-of ?Y
translated to axiom pattern
?X subClassOf: part-of some ?Y
-> Nucleus subClassOf: part-of some Cell

mercaptopurine
[pharmgkb:PA450379]
mercaptopurine
[drugbank:DB01033]
purine-6-thiol
[CHEBI:2208]
Class Equivalence
mercaptopurine
[ATC:L01BB02]
Top Level Classes
(disjointness)
drug diseasegenepathway
property chains
Class subsumption
mercaptopurine
[mesh:D015122]
Reciprocal
Existentials
drug disease
pathway gene
Formalized as an OWL-EL ontology
650,000+ classes, 3.2M subClassOf axioms, 75,000
equivalentClass axioms

Benefits: Enhanced Query Capability
– Use any mapped terminology to query a target resource.
– Use knowledge in target ontologies to formulate more
precise questions
• ask for drugs that are associated with diseases of the joint:
‘Chikungunya’ (do:0050012) is defined as a viral infectious disease
located in the ‘joint’ (fma:7490) and caused by a ‘Chikungunya
virus’ (taxon:37124).
– Learn relationships that are inferred by automated
reasoning.
• alcohol (ChEBI:30879) is associated with alcoholism (PA443309)
since alcoholism is directly associated with ethanol (CHEBI:16236)
• ‘parasitic infectious disease’ (do:0001398) retrieves 129 disease
associated drugs, 15 more than are directly associated.

Knowledge Discovery through Data
Integration and Enrichment Analysis
• OntoFunc: Tool to discover significant associations between sets of objects
and ontology categories. enrichment of attribute among a selected set of
input items as compared to a reference set. hypergeometric or the
binomial distribution, Fisher's exact test, or a chi-square test.
• We found 22,653 disease-pathway associations, where for each pathway
we find genes that are linked to disease.
– Mood disorder (do:3324) associated with Zidovudine Pathway
(pharmgkb:PA165859361). Zidovudine is used to treat HIV/AIDS. Side
effects include fatigue, headache, myalgia, malaise and anorexia
• We found 13,826 pathway-chemical associations
– Clopidogrel (chebi:37941) associated with Endothelin signaling
pathway (pharmgkb:PA164728163). Clopidogrel inhibits platelet
aggregation and prolongs bleeding time. Endothelins are proteins that
constrict blood vessels and raise blood pressure.

Tactical Formalization + Automated Reasoning
Offers Compelling Value for Certain Problems
We need to be smart about the goal, and how best to achieve it.
Tactical formalization is another tool in the toolbox.
We’ve formalized data as OWL ontologies:
• To identify mistakes in human curated knowledge
• To identify conflicting meaning in terms
• To identify mistakes in the representation of RDF data
o incorrect use of relations
o incorrect assertion of identity (owl:sameAs)
• To verify, fix and exploit Linked Data through expressive OWL
reasoning
• To generate/infer new triples to write back into RDF and use for
efficient retrieval
Many other applications can be envisioned.

Acknowledgements
Bio2RDF Release 2:
Allison Callahan, Jose Cruz-Toledo, Peter Ansell
Aberrant Pathways: Robert Hoehndorf, Georgios Gkoutos

dumontierlab.com
michel.dumontier@stanford.edu
Website: http://dumontierlab.com
Presentations: http://slideshare.com/micheldumontier

Tactical Formalization of Linked Open Data (Ontology Summit 2014)

More Related Content

Viewers also liked (20)

Similar to Tactical Formalization of Linked Open Data (Ontology Summit 2014) (20)

More from Michel Dumontier (20)

Recently uploaded (20)

Tactical Formalization of Linked Open Data (Ontology Summit 2014)