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Ontology Databases:
Detecting Inconsistencies in the Gene Ontology
using Not-gadgets
Paea LePendu
University of Oregon
Talk: National Center for Biomedical Ontology ◦ Stanford University ◦ September, 2009
General Interests
Logic
Programming
Languages
Automated
Reasoning
Databases
Outline
• Ontology-based Data Management
– Background, Motivation
– Theory
– Benchmarking
– Application Domain, Query Answering
• Inconsistency Detection
– Theory
– The serotonin example
– GO plus ZFIN, MGI annotations
Ontology-based Database Integration:
reducing database integration to ontology translation
Ontology-based Database Integration:
reducing database integration to ontology translation
Ontology-based Data Management
Ontology-based Data Management
User
Ontology
Data Access Layer
Data Annotation
Data Management
RDBMS
RDBMS
RDBMS
RDBMS
RDBMS
Example: sisters-siblings
This is what we know :
All sisters are siblings.
Hilary and Lynn are sisters.
This is what we want to know :
Who are siblings?
{ <x,y> | siblingOf(x,y) }
Obviously, the answer should be :
Hilary and Lynn are siblings.
{ <Hilary, Lynn> }
Example: sisters-siblings
Example: sisters-siblings
Example: The Gene Ontology
GO_0003674
z01, z02, z03
GO_0005488
e01,e02, e03
GO_0030528
y01, y02, y03
GO_0003676
c01, c02, c03
GO_0045182
b01, b02, b03
GO_0003677
x01, x02, x03
GO_0003723
d01, d02, d03
GO_0003700
w01, w02, w03
GO_0008135
a01, a02, a03
Example: The Gene Ontology
GO_0003674
z01, z02, z03
GO_0005488
e01,e02, e03
GO_0030528
y01, y02, y03
GO_0003676
c01, c02, c03
GO_0045182
b01, b02, b03
GO_0003677
x01, x02, x03
GO_0003723
d01, d02, d03
GO_0003700
w01, w02, w03
GO_0008135
a01, a02, a03
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
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
v_Person
Person
P-0004
Person
v_Female
Female
Male
v_Male
Female
Male
P-0002
P-0001
P-0003
[Pan & Heflin. DLDB: Extending Relational Databases to Support Semantic Web Queries. ISWC, 2003.]
Ontology Databases:
Active Database Approach
ON INSERT into Male
INSERT into Person
On INSERT into Female
INSERT into Person
Person
Person
Female
Male
Female
Male
[LePendu, et al. Ontology Database: a New Method for Semantic Modeling and an Application to
Brainwave Data. SSDBM, 2008.]
Ontology Databases:
Active Database Approach
ON INSERT into Male
INSERT into Person
On INSERT into Female
INSERT into Person
Person
Person
Female
Male
Female
Male
P-0001
Ontology Databases:
Active Database Approach
Person
P-0001
Person
Female
Male
Female
Male
P-0001
Ontology Databases:
Active Database Approach
Person
P-0001
Person
Female
Male
Female
Male
P-0002
P-0001
Ontology Databases:
Active Database Approach
Person
P-0001
P-0002
Person
Female
Male
Female
Male
P-0002
P-0001
Ontology Databases:
Active Database Approach
Person
P-0001
P-0002
Person
Female
Male
Female
Male
P-0002
P-0001
P-0003
Ontology Databases:
Active Database Approach
Person
P-0001
P-0002
P-0003
Person
Female
Male
Female
Male
P-0002
P-0001
P-0003
Ontology Databases:
Active Database Approach
Person
P-0001
P-0002
P-0003
Person
P-0004
Female
Male
Female
Male
P-0002
P-0001
P-0003
Example: sisters-siblings (revisited)
This is what we know :
All sisters are siblings.
Hilary and Lynn are sisters.
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)
This is what we know :
SiblingOf
All sisters are siblings.
Hilary and Lynn are sisters.
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
SisterOf
Hilary
Lynn
Example: sisters-siblings (revisited)
This is what we know :
SiblingOf
Hilary
Lynn
All sisters are siblings.
Hilary and Lynn are sisters.
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
SisterOf
Hilary
Lynn
Example: sisters-siblings (revisited)
This is what we know :
SiblingOf
Hilary
Lynn
All sisters are siblings.
Hilary and Lynn are sisters.
SisterOf
Hilary
Lynn
This is what we want to know :
Who are siblings?
Obviously, the answer should be :
Hilary and Lynn are siblings.
{ <x,y> | siblingOf(x,y) }
Example: sisters-siblings (revisited)
This is what we know :
SiblingOf
Hilary
Lynn
All sisters are siblings.
Hilary and Lynn are sisters.
SisterOf
Hilary
Lynn
This is what we want to know :
Who are siblings?
{ <x,y> | siblingOf(x,y) }
Just look it up!
Obviously, the answer should be :
Hilary and Lynn are siblings.
Example: sisters-siblings (revisited)
This is what we know :
SiblingOf
Hilary
Lynn
All sisters are siblings.
Hilary and Lynn are sisters.
SisterOf
Hilary
Lynn
This is what we want to know :
Who are siblings?
{ <x,y> | siblingOf(x,y) }
Just look it up!
Obviously, the answer should be :
Hilary and Lynn are siblings.
{ <Hilary, Lynn> }
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.]
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]
Ontology-based Query Answering
Return 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.
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
Not-gadgets
¬
→
¬
Example: inconsistency detection
"Annotations in this way sometimes point to errors in the typetype 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]
Example: serotonin secretion
gene-x
not-gadget
fail!
gene-x
Example: GO:0004931
ATP-gated cation channel activity (as of 3/09):
[Term]
id: GO:0004931
name: ATP-gated cation channel activity
namespace: molecular_function
def: "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 activity
is_a: GO:0005261 ! cation channel activity
Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
Example: GO:0004931
What is so interesting about GO:0004391?
ZFIN ZDB-GENE-030319-2
p2rx2 NOT GO:0004931ZFIN:ZDBPUB-031031-8|PMID:14580944 IDA
F
purinergic
receptor P2X, ligand-gated ion channel, 2
gene
taxon:7955 20071005 ZFIN
ZFIN ZDB-GENE-030319-2
p2rx2
GO:0004931ZFIN:ZDBPUB-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/
Example: GO:0004931
The not-gadget will raise a logical inconsistency.


p2rx2 NOTGO:0004931
p2rx2
GO:0004931
GO_0004931
_GO_0004931
p2rx2
not-gadget
fail!
* Tables starting with an '_' are negations.
Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
Example: GO:0004931
GO:0004391 sub-graph (using Jambalaya):
ZFIN
ZFIN
MGI
MGI
MGI
ZFIN - MGI
ZFIN
Outcome: suspect IEA annotations
GO Online SQL Environment (GOOSE)
pos,IEA(graph_path
x association) x
neg(grapth_path
x association)
Source: [1/13/2009] http://www.geneontology.org/GO.database.shtml#diagram
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
IGI
ZFIN:ZDB-GENE-000427-3
F
purinergic
receptor P2X, ligand-gated ion channel, 2
gene
taxon:7955
20071005
ZFIN
– Perfectly admissible!
Next Directions
• Explanation and proof-reconstruction ✓
• Deep (data) annotation tools
• Distributed network of Ontology Databases
Data Annotation:
Neural ElectroMagnetic Ontologies
frontocentral
LFRON
RFRON
[Frishkoff, et al. ERP measures of partial
semantic knowledge: Left temporal indices of
skill differences and lexical quality. Biological
Psychology, 2009.]
Network of Ontology Databases
[Thorisson, Muilu and Brookes. Genotype–phenotype databases: challenges and solutions for the
post-genomic era. Nature Reviews, 2009.]
Thank you
Questions?
Andrea’s Example
Is John supervised by a TopManager
who is a friend of an AreaManager?
[Franconi. Ontologies and databases: myths and challenges. VLDB, 2008.]
Raymond Reiter
[Reiter. Deductive Question-Answering on Relational Data Bases. Logic and Data Bases, 1977]
Raymond Reiter
Raymond Reiter
Raymond Reiter
Benchmarking Suite
Origins
CIS @ UO
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
Ontology-based Data Access
[Rodriguez-Muro , et al. Realizing Ontology Based Data Access: A plug-in for protégé. ICDEW, 2008.]