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Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Action items highlighted in lime green!
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Action items highlighted in lime green!
Introductions: Who we are (1/3)
• NEMO “Core” (PIs & go-to people)– Dejing Dou (lead PI, CIS)– Gwen Frishkoff (co-PI, Psychology)– Allen Malony (co-I, CIS)– Don Tucker (co-I, Psychology)– Paea LePendu* (Ontology Development)– Robert Frank* (EEG/ERP Analysis Tools)– Jason Sydes* (Database & Wed Portal)– Haishan Liu (Grad Student, CIS)
• Matt Cranor & Charlotte Wise (Grants Admin)
Introductions: Who we are (2/3)
• NEMO Consortium– John Connolly (McMaster U)– Tim Curran (U Colorado)– Joe Dien (U Maryland)– Kerry Kilborn (Glasgow U)– Dennis Molfese (U Louisville)– Chuck Perfetti (U Pittsburgh)
• Please send link to your website to Jason ([email protected])
Introductions: Who we are (3/3)
• External collaborators (NEMO ontologies & database development; integration with other projects in BO community)– Jessica Turner (fBIRN & “CogPO” project)– Angela Laird (BrainMap & “CogPO” project)– Maryann Martone (NIF -- www.neuinfo.org)– Jeff Grethe & Scott Makeig (“HeadIT” project)– Folks at OBOF (http://www.obofoundry.org/)?– Folks at NCBO (http://bioontology.org/)?
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Regular Meetings
• Schedule using Doodlehttp://www.doodle.com/
• Once monthly?• Gwen to propose dates & times on Doodle for
next month’s meeting later today• Please respond to Doodle email (click on link
and check available days & times)
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Overview of Project Aims1. Design and test procedures for automated ERP pattern
analysis and classification (*)– “top-down” initial definitions of pattern rules, concepts
(hypotheses)– “bottom-up” data mining for pattern validation & refinement
2. Capture rules, concepts in a formal ERP ontology (TODAY)3. Develop ontology-based tools for ERP data markup (*)4. Apply ERP analysis tools to consortium datasets (*)5. Perform meta-analyses of consortium data (*)6. Build relational database to store ontology-based
annotations and to support complex reasoning over annotated data
“ontology database”7. Build data storage & management system
“EEG database”
(*) Proposed focus of next month’s meeting
The three pillars of NEMO
• Ontologies (TODAY)• Ontology-based analysis tools (next time?)• Ontology database & portal
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Contributing to NEMO• NEMO central
– http://nemo.nic.uoregon.edu• NEMO ftp site (EEG database)
– ftp://nemo.nic.uoregon.edu/EEG_Experiments• NEMO sourceforge (ontologies)
– http://nemoontologies.svn.sourceforge.net/viewvc/nemoontologies/current/
• NEMO listserve (to note ontology “bugs” and feature requests)– http://sourceforge.net/mail/?group_id=263320
• NEMO wiki (discussion) – coming soon…
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of tools for labeling data (next time)
Why (what problem are we trying to solve?)
What (what IS an ontology anyway, and how can it help address this problem?)
How (ERP ontology design and implementation methods in NEMO)
NEMO ontology development
410 ms
450 ms
330 ms
Peak latency 410 ms
Loose Semantics!
Will the “real” N400 please step forward?
Sample Database Query: Show me all the N400 patterns in the database.
How we’re going to build ontologies for NEMO
[…and apply them to real data – next time]
FIRST RELEASE OF ONTOLOGIES IN AUGUST (DON’T BOTHER TO
COMMENT ON OLD VERSIONS…)
NEMO ontology design principles(following OBO “best practices”)
1. Factor the domain to generate modular (“orthogonal”) ontologies that can be reused, integrated for other projects
2. Reuse existing ontologies (esp. foundational concepts) to define basic (upper & mid-level) concepts
3. Validate definitions of complex concepts using bottom-up (data-driven) as well as top-down (knowledge-driven) methods
4. Collaborate with a community of experts in collaborative design, testing of ontologies
Factoring the ERP domain
1 sec
TIME SPACE
FUNCTION Modulation of pattern features (time,
space, amplitude) under different experiment conditions
ERP spatial subdomain
1 sec
TIME SPACE
FUNCTION Modulation of ERP pattern features under different experiment conditions
NEMO Spatial Ontology
BFO (Basic Formal
Ontology) UPPER
FMA(Foundational
Model of Anatomy ontology)
MIDLEVEL
SNAP
ERP temporal subdomain
1 sec
TIME SPACE
FUNCTION Modulation of ERP pattern features under different experiment conditions
Early (“exogenous”) vs. Late (“endogenous”) ERP processes
~0-150 ms after event (e.g., stimulus onset)
501 ms or more after event (e.g., stimulus onset)
~151-500 after event (e.g., stimulus onset)
EARLY
LATE
MID-LATENCY
ERP functional subdomain
1 sec
TIME SPACE
FUNCTION Modulation of ERP pattern features under different experiment conditions
NEMO Functional Ontology
Angela LairdBrainMap
Jessica TurnerBIRNlex
(now part of Neurolex)
CogPO
http://brainmap.org/scribe/index.html
Reconsistituting the ERP domain…
1 sec
TIME SPACE
FUNCTION Modulation of ERP pattern features under different experiment conditions
NEMO ERP Ontology
Observed Pattern = “P100” iff Event type is stimulus AND
FUNCTIONAL Peak latency is between 70 and 140 ms AND
TEMPORAL Scalp region of interest (ROI) is occipital AND SPATIAL Polarity over ROI is positive (>0)
FUNCTION TIME SPACE
PATTERN DEFINITIONS (Revised)
“P100” 1. 70 ms < TI-max ≤ 140 ms2. ROI = Occipital3. IN-mean (ROI) > 0
“N100” 1. 141 ms < TI-max ≤ 220 ms2. ROI = Occipital3. IN-mean (ROI) < 0
“N3c” 1. 221 ms < TI-max ≤ 260 ms2. ROI = Anterior Temporal3. IN-mean (ROI) < 0
“MFN” 1. 261 ms < TI-max ≤ 400 ms2. ROI = Mid Frontal3. IN-mean (ROI) < 0
“P300” 1. 401 ms < TI-max ≤ 600 ms2. ROI = Parietal3. IN-mean (ROI) > 0
SPATIAL TEMPORAL
Cycles of pattern definition, validation, & refinement(MORE ON THIS NEXT TIME…)
Frishkoff, Frank, et al., 2007
Overview Agenda
• Introductions & go-to people (7 mins)• Scheduling regular teleconferences (3 mins)• Review of project aims (15 mins)• Contributing to NEMO -- overview (10 mins)
(website, wiki, database)• Overview of current ontologies (25 mins)• Overview of RDF/OWL annotation (Dejing Dou)
43
An Introduction for Annotation• Annotation and Markup
– HTML – XML/RDF/OWL
• Ontology-based Annotation– Ontologies and Data Tables. – Links of Data and Ontological Concepts – Applications
Reference: Siegfried Handschuh, Steffen Staab, Raphael Volz: On deep annotation. WWW 2003: 431-438
44
Annotation and Markup• The idea of Annotation or Markup came from WWW. HTML,
Hypertext Markup Language, is still a well-used markup language. For example, your personal homepage are very possibly written in HTML:
<html> <head> <title>Dejing Dou’s
Homepage</title> </head> <body> …. </body> </html> The tags (annotators) (e.g., title, body..) are well defined and
computer can process and display the text, images …in preferred places, color and font size.
XML/RDF/OWL• The XML, eXtensible Markup Language, lets users self-define new
tags: <?xml version="1.0" encoding='UTF-8'?> <faculty> <name>Dejing Dou</name> <ranking> Assistant Professor
</ranking> <student> Paea Lependu </student> …. </faculty> I defined those new tags (faculty, name, ranking…)
but computer do not know the meaning or the semantics of them.
• Using similar syntax, RDF (Resource Definition Framework) and OWL (Web Ontology Language) allow users to define the semantics of tags as ontologies.
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A Simple Ontology of University
46
People
Faculty
StaffStudent Assistant Prof.
Associate Prof.
Full Prof.
StringName
Graduate Student Undergraduate
Is_a Is_a
Is_a
Is_a Is_a Is_a
Is_a Is_a
Stringtitle
Numberage
47
Sample Data on the People
School_ID Name Age Title Ranking
950499879
D. Dou 36 Dr. Assistant Professor
950699887
P. LePendu 34 Graduate Student
… … … … …
Data and Ontology
48
School_ID Name Age Title Ranking
950499879 D. Dou 36 Dr. Assistant Professor
950699887 P. LePendu 34 Graduate Student
… … … … …People
Faculty
StaffStudent Assistant
Prof.
Associate Prof.
Full Prof.
String
Graduate Student
Undergraduate
Is_a Is_a
Is_a
Is_aIs_a
Is_a
Is_a Is_a
String title
Numberage
Name
Ontology-based Annotation: the links
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School_ID Name Age Title Ranking
950499879 D. Dou 36 Dr. Assistant Professor
950699887 P. LePendu 34 Graduate Student
… … … … …People
Faculty
StaffStudent Assistant
Prof.
Associate Prof.
Full Prof.
String
Name
Graduate Student
Undergraduate
Is_a Is_a
Is_a
Is_aIs_a
Is_a
Is_a Is_a
String title
Numberage
Results In RDF/OWL • Computer can process it automatically: <People rdf:ID=“950499879”>
<name>Dejing Dou</name>
<age>36</age>
<title> Dr. </title>
<ranking rdf:resource="#Assistant Professor"/>
</People>
<People rdf:ID=“950699887”>
<name>Paea Lependu</name>
<age>34</age>
<ranking rdf:resource="#Graduate Student"/>
</People>
… 50
What we can do?• Search
– Example: return all data rows related to faculty (i.e., all data of assistant, associate and full professors will be returned.)
• Query– Examples: Give the average age of assistant and associate professors only?What are the difference of age range between faculty and
students? • In NEMO, we will develop ontology-based tools to automatically
answer:Return all PCA factors related to “P100” and “N100” only (Search)
What are the difference of range of time latency between Lab A and Lab B’s “P100” patterns in the same paradigm X ? (Query)
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