August 9, 2010

NEMO Teleconference:Progress summary &

Year II planning

http://nemo.nic.uoregon.edu

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database & portal)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database & portal)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

The big picture

The big picture

Three areas where partner sites can contribute over next 6-8 weeks

• Ontology: Help vet ERP pattern definitions (based on knowledge of literature)– What: Get list of ERP patterns from Gwen & provide feedback

(via email/sourceforge tracker)– When: Immediately

• Data: Help test and refine data analysis tools– What: Configure SVN software link to sourceforge. Download & test latest

NEMO pattern extraction (PCA/ICA/microstate) and metric extraction tools– When: Next week(ish)… Bob to send email when ready

• Database: Help test and refine portal interface– What: Get link to latest MI portal mockup from Jason & give feedback (via

email/sourceforge tracker). Later: upload actual datasets.– When: Next week(ish)… Jason to send email when ready

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database & portal)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

MI Checklist:MIBBI/MINI collaboration

CARMEN Project: Development of MINI

Frank Gibson & colleagues

MINEMO: MI for NEMO (Experiment Level I)

MINEMO: MI for NEMO (Experiment Level II)

MINEMO: MI for NEMO (Subject Group I)

MINEMO: MI for NEMO (Subject Group II)

MINEMO: MI for NEMO (Task Level)

MINEMO: MI for NEMO (Stimulus Level I)

MINEMO: MI for NEMO (Stimulus Level II)

MINEMO: MI for NEMO (Response Level I)

MINEMO: MI for NEMO (Contrast Level I)

MINEMO: MI for NEMO (Contrast Level II)

MINEMO: MI for NEMO (EEG Data Level I)

MINEMO: MI for NEMO (EEG Data Level II)

MINEMO: MI (Expt metadata) Portal Interface

• Definition• Concept label & ID• Data Entry instructions• (if not obvious from definition)

Note: This is on wish

list…

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

What’s an ontology & what’s it for?“Highly semantically

structured”

What does this mean & what

does it buy us?

The practical goal is to formulate pattern definitions and encode them in ontology. We can then use pattern rules to classify data, and ultimately to revise them based on meta-

analysis results

Observed Pattern = “N400” iff

Event type is onset of meaningful stimulus (e.g., word) AND

Peak latency is between 300 and 500 ms AND

Scalp region of interest (ROI) is centroparietal AND

Polarity over ROI is negative(>0)

Ontologies for high-level, explicit

representation of domain knowledge

theoretical integration

RDF to support principled data classfication & meta-analysispractical integration

Coding knowledge of patterns in ontology

Partial rule for classification of a

particular ERP observation as an

instance of the N2 class

What is RDF and what is it for?RDF graph

(data model)

The big picture

Typical tabular representation of summary ERP data

Peak latency measurement

ERP observation (pattern extracted from “raw” ERP data)

Annoting ERP data: From Spreadsheets to RDF

Pattern Labels

Functional attributes

Temporal attributes

Spatial attributes= + +

Robert M. Frank

Concepts coded in OWL NEMO ontology

Data coded in RDF NEMO database

HOW?

Annotating Data in RDF• Data Annotation

– The process of marking up or “tagging” data with meaningful symbols; tags may come from ontology linked to a URI

• URI (Uniform Resource Identifier)– A compact sequence of characters that identifies an abstract

or physical resource (typically located on the Web)

• RDF (Resource Description Framework)– RDF is a directed, labeled graph (data model) for representing

information (typically on the Web)

*See Glossary (http://www.seiservices.com/nida/1014080/ReadingRoom.aspx)

The “RDF Triple”In RDF form: <001> <type> <NEMO_0000093>

Subject – Predicate –Object In natural language:

The data represented in row A is an instance of (“type” = “is a”) some ERP pattern.

That is, measurements (cells) are “about” ERP patterns (rows).

In graph form:

RDF Triple #2

In natural language =

The data represented in cell Z (row A, column 1) is an instance of (“is a”) a peak latency temporal measurement (i.e., the time at which the pattern is of maximal amplitude)

In RDF form: <002> <type> <NEMO_0745000>

Subject – Predicate –Object

RDF Triple #3

This graph represents an assertion, expressed in RDF =<001> <is_peak_latency_measurement_of> <002>

The data represented in cell Z is a temporal property of the ERP pattern represented in row A

RECALL: Pattern definition is encoded in the ontology (not in RDF data rep!)

HOW?

An ERP pattern rule (temporal criterion) as it appears in Protégé

Protégé rendering

OWL/RDF rendering

RECALL: The goal is to formulate pattern definitions, use them to classify data, and ultimately to revise them

based on meta-analysis results

Observed Pattern = “N400” iff

Event type is onset of meaningful stimulus (e.g., word) AND

Peak latency is between 300 and 500 ms AND

Scalp region of interest (ROI) is centroparietal AND

Polarity over ROI is negative(>0)

This is the inference that we want to make

In RDF terms…

Linking data to ontology — Step 1:A worked example (Formulating rule)

First, we write the ERP pattern rule as follows:

IF (1) 001 type ERP_spatiotemporal pattern• and (2) 002 type peak_latency_measurement_datum• and (3) 002 is_peak_latency_measurement_of 001,• and (4) 002 has_numeric_value X,• and (5) 500 >= X >= 300 (X has datatype decimal)

(in reality, there are spatial, temporal, & functional criteria…)

THEN (6) 001 type N400_pattern

Next, we convert the rule to a SPARQL query by replacing natural language terms with corresponding URI (tags) from NEMO ontology

• type rdf:type • ERP_spatiotemporal_pattern NEMO_0000093• peak_latency_measurement NEMO_0745000• is_measurement_of NEMO_9278000• has_numeric_value NEMO_7943000

Linking data to ontology — Step 2:Formulating rule as SPARQL query

Finally, we load Virtuoso’s SPARQL interface http://nemo.nic.uoregon.edu:8890/sparql

& then cut and paste the query into the Query textbox and click Run Query.

…. And Virtuoso returns the following results (for ex):

As a result, we can deduce that ERP observations 0002, 0003, 0004, & 0006 are

N400 pattern instances… QED

Linking data to ontology — Step 3:Executing the SPARQL query

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

Need to vet ERP pattern rules in ontology!

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

Overview Agenda

1. Planning for 2nd Annual All-Hands meeting2. Year 2 milestones

– Experiment Management System (database)– Relational (RDF) database & use of ontologies to

classify, query data– Encoding ERP patterns and pattern rules in ontology– Next steps for data analysis:

• Clustering within and across dataset• Source localization

3. Milestones to shoot for prior to next AHM

What next? Commitments from individual sites; task assignments

• Ontology: Help vet ERP pattern definitions (based on knowledge of literature)– What: Get list of ERP patterns from Gwen & provide feedback

(via email/sourceforge tracker)– When: Immediately

• Data: Help test and refine data analysis tools– What: Configure SVN software link to sourceforge. Download & test latest

NEMO pattern extraction (PCA/ICA/microstate) and metric extraction tools– When: Next week(ish)… Bob to send email when ready

• Database: Help test and refine portal interface– What: Get link to latest MI portal mockup from Jason & give feedback (via

email/sourceforge tracker). Later: upload actual datasets.– When: Next week(ish)… Jason to send email when ready