Ontology Databases: Detecting Inconsistencies in the Gene Ontology using Not-gadgets Paea LePendu...
If you can't read please download the document
Ontology Databases: Detecting Inconsistencies in the Gene Ontology using Not-gadgets Paea LePendu University of Oregon Talk: National Center for Biomedical
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
Slide 2
General Interests Programming Languages Automated Reasoning
Databases Logic
Slide 3
Outline Ontology-based Data Management Background, Motivation
Theory Benchmarking Application Domain, Query Answering
Inconsistency Detection Theory The serotonin example GO plus ZFIN,
MGI annotations
Slide 4
Ontology-based Database Integration: reducing database
integration to ontology translation
Slide 5
Slide 6
Ontology-based Data Management
Slide 7
Ontology User Data Annotation Data Management Data Access
Layer
Slide 8
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? { } Obviously, the answer should be :
Hilary and Lynn are siblings. { | siblingOf(x,y) }
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
Slide 14
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.]
Slide 15
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.] ON
INSERT into Male INSERT into Person On INSERT into Female INSERT
into Person
Slide 16
Ontology Databases: Active Database Approach Person Female Male
ON INSERT into Male INSERT into Person On INSERT into Female INSERT
into Person
Slide 17
Ontology Databases: Active Database Approach Person Female
Male
Slide 18
Ontology Databases: Active Database Approach Person Female
Male
Slide 19
Ontology Databases: Active Database Approach Person Female
Male
Slide 20
Ontology Databases: Active Database Approach Person Female
Male
Slide 21
Ontology Databases: Active Database Approach Person Female
Male
Slide 22
Ontology Databases: Active Database Approach Person Female
Male
Slide 23
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)
Slide 24
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)
Slide 25
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)
Slide 26
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(x,y) }
Slide 27
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(x,y) } Just
look it up!
Slide 28
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(x,y) } Just
look it up! { }
Slide 29
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.]
Slide 30
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]
Slide 31
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.
Slide 32
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
Slide 33
Not-gadgets
Slide 34
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]
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 What is so interesting about GO:0004391?
ZFINZDB-GENE-030319-2p2rx2NOTGO:0004931ZFIN:ZDB-
PUB-031031-8|PMID:14580944IDAFpurinergic receptor P2X, ligand-gated
ion channel, 2gene taxon:795520071005ZFIN
ZFINZDB-GENE-030319-2p2rx2GO:0004931ZFIN:ZDB-
PUB-031031-8|PMID:14580944IGIZFIN:ZDB-GENE-000427-3 Fpurinergic
receptor P2X, ligand-gated ion channel, 2
genetaxon:795520071005ZFIN Source: [1/13/2009]
http://www.geneontology.org/gene-associations/http://www.geneontology.org/gene-associations/
Slide 39
Example: GO:0004931 The not-gadget will raise a logical
inconsistency. p2rx2NOTGO:0004931 p2rx2GO:0004931 GO_0004931 *
Tables starting with an '_' are negations. not-gadget fail!
_GO_0004931 p2rx2 _GO_0004931 p2rx2
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)
Slide 52
What do logical inconsistencies mean? Several possibilities:
Incorrect annotation (e.g., suspect IEA annotations) Incorrect
relationship (e.g., serotonin secretion) Incomplete model: Recall:
ZFINZDB-GENE-030319-2p2rx2GO:0004931
ZFIN:ZDB-PUB-031031-8|PMID:14580944IGI
ZFIN:ZDB-GENE-000427-3Fpurinergic receptor P2X, ligand-gated ion
channel, 2gene taxon:795520071005ZFIN Perfectly admissible!
Slide 53
Next Directions Explanation and proof-reconstruction Deep
(data) annotation tools Distributed network of Ontology
Databases
Slide 54
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.]
Slide 55
Network of Ontology Databases [Thorisson, Muilu and Brookes.
Genotypephenotype databases: challenges and solutions for the
post-genomic era. Nature Reviews, 2009.]
Slide 56
Thank you Questions?
Slide 57
Slide 58
Andreas Example Is John supervised by a TopManager who is a
friend of an AreaManager? [Franconi. Ontologies and databases:
myths and challenges. VLDB, 2008.]
Slide 59
Raymond Reiter [Reiter. Deductive Question-Answering on
Relational Data Bases. Logic and Data Bases, 1977]
Slide 60
Raymond Reiter
Slide 61
Slide 62
Slide 63
Slide 64
Benchmarking Suite
Slide 65
Origins
Slide 66
CIS @ UO
Slide 67
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
Slide 68
Ontology-based Data Access [Rodriguez-Muro, et al. Realizing
Ontology Based Data Access: A plug-in for protg. ICDEW, 2008.]