Ontology Databases: Detecting Inconsistencies in the Gene Ontology using Not-gadgets
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Ontology Databases:Detecting Inconsistencies in the Gene Ontology using
Not-gadgets
Paea LePenduUniversity of Oregon
Talk: National Center for Biomedical Ontology ◦ Stanford University ◦ September, 2009
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General Interests
Programming Languages
Automated Reasoning
Databases
Logic
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Outline
• Ontology-based Data Management– Background, Motivation– Theory– Benchmarking– Application Domain, Query Answering
• Inconsistency Detection– Theory– The serotonin example– GO plus ZFIN, MGI annotations
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Ontology-based Database Integration:reducing database integration to ontology translation
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Ontology-based Database Integration:reducing database integration to ontology translation
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RelationAttributeDatatype
KeysConstraint
ViewTriggerTuple
Ontology-based Data Management
ClassPropertyDatatypeSubClass
RestrictionIndividual
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Ontology-based Data Management
Ontology
RDBMS
User
RDBMSRDBMS
RDBMSRDBMS
Data Annotation Data Management
Data Access Layer
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Example: sisters-siblings
All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
{ <Hilary, Lynn> }
Obviously, the answer should be :
Hilary and Lynn are siblings.
{ <x,y> | siblingOf(x,y) }
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Example: sisters-siblings
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Example: sisters-siblings
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Example: The Gene OntologyGO_0003674
z01, z02, z03
GO_0005488e01,e02, e03
GO_0030528y01, y02, y03
GO_0003677x01, x02, x03
GO_0003700w01, w02, w03
GO_0003676c01, c02, c03
GO_0003723d01, d02, d03
GO_0008135a01, a02, a03
GO_0045182b01, b02, b03
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Example: The Gene OntologyGO_0003674
z01, z02, z03
GO_0005488e01,e02, e03
GO_0030528y01, y02, y03
GO_0003677x01, x02, x03
GO_0003700w01, w02, w03
GO_0003676c01, c02, c03
GO_0003723d01, d02, d03
GO_0008135a01, a02, a03
GO_0045182b01, b02, b03
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Ontology Databases:General Models for Database Designs
• Generality is important– Avoid rewriting
• Scalability of KB is important– Persistence, caching and indexing
• Major generic models– Horizontal Models– Vertical Models– Decomposition Storage Models
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Ontology Databases: View-based Approach
CREATE VIEW v_Person(id) AS SELECT id FROM Person UNION SELECT id FROM v_Male UNION SELECT id FROM v_Female
Person
Female Male
[Pan & Heflin. DLDB: Extending Relational Databases to Support Semantic Web Queries. ISWC, 2003.]
v_Person
Person
P-0004
FemaleP-0002
MaleP-0001
P-0003
v_Female v_Male
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Ontology Databases: Active Database Approach
Person
Female Male
[LePendu, et al. Ontology Database: a New Method for Semantic Modeling and an Application to Brainwave Data. SSDBM, 2008.]
Female Male
Person
ON INSERT into Male INSERT into Person
On INSERT into Female INSERT into Person
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Ontology Databases: Active Database Approach
Person
Female Male Female MaleP-0001
Person
ON INSERT into Male INSERT into Person
On INSERT into Female INSERT into Person
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Ontology Databases: Active Database Approach
Person
Female Male Female MaleP-0001
PersonP-0001
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Ontology Databases: Active Database Approach
Person
Female Male FemaleP-0002
MaleP-0001
PersonP-0001
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Ontology Databases: Active Database Approach
Person
Female Male FemaleP-0002
MaleP-0001
PersonP-0001P-0002
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Ontology Databases: Active Database Approach
Person
Female Male FemaleP-0002
MaleP-0001P-0003
PersonP-0001P-0002
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Ontology Databases: Active Database Approach
Person
Female Male FemaleP-0002
MaleP-0001P-0003
PersonP-0001P-0002P-0003
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Ontology Databases: Active Database Approach
Person
Female Male FemaleP-0002
MaleP-0001P-0003
PersonP-0001P-0002P-0003P-0004
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)SiblingOf
SisterOf
Hilary Lynn
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)SiblingOf
Hilary Lynn
SisterOf
Hilary Lynn
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)SiblingOf
Hilary Lynn
SisterOf
Hilary Lynn
{ <x,y> | siblingOf(x,y) }
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)SiblingOf
Hilary Lynn
SisterOf
Hilary Lynn
{ <x,y> | siblingOf(x,y) }
Just look it up!
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All sisters are siblings.
Hilary and Lynn are sisters.
This is what we know :
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)SiblingOf
Hilary Lynn
SisterOf
Hilary Lynn
{ <x,y> | siblingOf(x,y) }
Just look it up!
{ <Hilary, Lynn> }
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Lehigh University Benchmark (LUBM)Load Time and Query Time
(1.5 million facts)(10 Universities, 20 Departments)
[Guo, et al. LUBM: A Benchmark for OWL Knowledge Base Systems. J Web Semantics, 2005.]
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Ontology-based Data Management
[Frishkoff, et al. Development of Neural Electromagnetic Ontologies (NEMO): Ontology-based Tools for Representation and Integration of Event-related Brain Potentials. ICBO, 2009]
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Ontology-based Query AnsweringReturn all data instances that belong to ERP pattern classes which have a surface positivity over
frontal regions of interest and are earlier than the N400.
Which patterns have a region of interest that is left-occipital and manifests between 220 and 300ms?
What is the range of intensity mean for the region of interest for N100?
Show the region of interest for all ERP patterns that occur between 0 and 300ms.
Which PCA factor do P100 patterns most often appear in?
What is the range of intensity mean for the region of interest for N100 patterns?
Show the patterns whose region of interest is left occipital and occurs between 220 and 300ms.
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Inconsistency Detection
• Background and Motivation– Expressiveness– From disjunctions to negations
• Theory– Not-gadgets
• Motivation– Serotonin example– ATP-gated cation channel activity
• Results from ZFIN and MGI Annotations
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Not-gadgets
¬ →
¬
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Example: inconsistency detection
"Annotations in this way sometimes point to errors in the type-type relationships described in the ontology. An example is the recent removal of the type serotonin secretion as an is_a child of neurotransmitter secretion from the GO Biological Process ontology. This modification was made as a result of an annotation from a paper showing that serotonin can be secreted by cells of the immune system where it does not act as a neurotransmitter.“
[Hill, et al. Gene Ontology annotations: what they mean and where they come from. BMC Bioinformatics, 2008]
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gene-x
gene-x
not-gadgetfail!
Example: serotonin secretion
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Example: GO:0004931
ATP-gated cation channel activity (as of 3/09):[Term]id: GO:0004931name: ATP-gated cation channel activitynamespace: molecular_functiondef: "Catalysis of the transmembrane transfer of an ion by a channel that opens when extracellular ATP has been bound by the channel complex or one of its constituent parts." [GOC:mah, PMID:9755289]comment: Note that this term refers to an activity and not a gene product. Consider also annotating to the molecular function term 'purinergic nucleotide receptor activity ; GO:0001614'.synonym: "P2X activity" RELATED []synonym: "purinoceptor" BROAD []synonym: "purinoreceptor" BROAD []is_a: GO:0005231 ! excitatory extracellular ligand-gated ion channel activityis_a: GO:0005261 ! cation channel activity
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Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
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Example: GO:0004931
What is so interesting about GO:0004391?
ZFIN ZDB-GENE-030319-2 p2rx2 NOT GO:0004931ZFIN:ZDB-PUB-031031-8|PMID:14580944 IDA F
purinergic receptor P2X, ligand-gated ion channel, 2gene taxon:7955 20071005 ZFIN
ZFIN ZDB-GENE-030319-2 p2rx2 GO:0004931ZFIN:ZDB-PUB-031031-8|PMID:14580944 IGI ZFIN:ZDB-GENE-000427-3 F purinergic receptor P2X, ligand-gated ion channel, 2 gene taxon:7955 20071005
ZFIN
Source: [1/13/2009] http://www.geneontology.org/gene-associations/
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Example: GO:0004931
The not-gadget will raise a logical inconsistency. p2rx2 NOTGO:0004931 p2rx2 GO:0004931
GO_0004931
* Tables starting with an '_' are negations.
not-gadgetfail!_GO_0004931
p2rx2
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Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
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Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
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Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
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ZFIN
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ZFIN
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MGI
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MGI
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MGI
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ZFIN - MGI
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ZFIN
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Outcome: suspect IEA annotations
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GO Online SQL Environment (GOOSE)
Source: [1/13/2009] http://www.geneontology.org/GO.database.shtml#diagram
pos,IEA(graph_path x association) x neg(grapth_path x association)
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What do logical inconsistencies mean?
• Several possibilities:– Incorrect annotation (e.g., suspect IEA annotations)– Incorrect relationship (e.g., serotonin secretion)– Incomplete model:
Recall:ZFIN ZDB-GENE-030319-2 p2rx2 GO:0004931
ZFIN:ZDB-PUB-031031-8|PMID:14580944 IGIZFIN:ZDB-GENE-000427-3 F purinergic receptor P2X, ligand-gated ion channel, 2 gene taxon:795520071005 ZFIN
– Perfectly admissible!
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Next Directions
• Explanation and proof-reconstruction ✓• Deep (data) annotation tools• Distributed network of Ontology Databases
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Data Annotation:Neural ElectroMagnetic Ontologies
LFRON
RFRON
frontocentral
[Frishkoff, et al. ERP measures of partial semantic knowledge: Left temporal indices of skill differences and lexical quality. Biological Psychology, 2009.]
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Network of Ontology Databases
[Thorisson, Muilu and Brookes. Genotype–phenotype databases: challenges and solutions for the post-genomic era. Nature Reviews, 2009.]
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Thank you
Questions?
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Andrea’s Example
Is John supervised by a TopManager who is a friend of an AreaManager?
{ {Mary/y, Andrea/z}, {Andrea/y, Paul/z} }[Franconi. Ontologies and databases: myths and challenges. VLDB, 2008.]
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Raymond Reiter
[Reiter. Deductive Question-Answering on Relational Data Bases. Logic and Data Bases, 1977]
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Raymond Reiter
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Raymond Reiter
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Raymond Reiter
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Benchmarking Suite
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Origins
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CIS @ UO
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CIS @ UO• Research Areas in Computer Science:
– software engineering– programming languages– human-computer interaction– parallel and distributed computing– networking and graph theory– scientific computation/visualization– information integration and mining
• Affiliates:– Neurosciences Institute– Computational Science Institute– Zebrafish Information Network
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Ontology-based Data Access
[Rodriguez-Muro , et al. Realizing Ontology Based Data Access: A plug-in for protégé. ICDEW, 2008.]