how semantic technologies can help to cure hearing loss?

Insight Centre for Data Analytics

How Semantic Technologies can

help to cure Hearing Loss

An Introduction to the SIFEM EU Project

André Freitas, Ratnesh Sahay

October 3rd, 2014

SIFEM Team

Kartik Asooja

João Jares

Marggie Jones

Oya Beyan

Yasar Khan

Stefan Decker

Ratnesh Sahay

André Freitas

Insight Centre for Data Analytics Slide 3

Outline

• Motivation: Modelling the Mechanics of Hearing

• Challenges in Contemporary Science

• Semantic Infrastructure

• Demonstration

• Take-away message


Goals

• Discuss the challenges that contemporary scientific

practice faces

• Discuss how Semantic Technologies can help.


Outside the computer science

community

Multi-scale Models


Finite Element Models (Video)


Geometrical Model


Physics Model

• FE equilibrium for solid

• FE equilibrium for fluid


Numerical Models/Solvers

• Incremental-iterative implicit solution scheme


Experimental Data

• A


Common Challenges for

‘Big Science’


Coordination Complexity (L. Floridi)


Reproducibility


Efficiency &

Automation


How to build an infrastructure which

addresses these dimensions?


Characteristics of the SIFEM Domain

• Most data is at the numeric level

• Highly dependent on visualization (man in the middle)



• Relatively small set of concepts



• But difficult to represent • Physics, geometrical models, topological relations, algoithmic,

mathematics


Semantic Infrastructure

• Coordination Complexity • Semantic Web standards and standardized vocabularies for

representing FE resources

• Simulation Platform with built-in standardized data representation

• One-stop shop for FE simulation resources (inner ear)

• Reproducibility • Web platform for sharing FE Simulations

• Simulation output as Linked Data

• Executable papers

• Efficiency & Automation • Facilitating data interpretation

• Attribution & Incentives • ORCID & Altmetrics


Lid-driven cavity flow


Physical Model

Solver

FEM Model

If there a vortex close to

the lid?

Automatic Interpretation


Expected physical behavior (Experiment intent):

Velocity in X starts at zero at the bottom of the box

followed by a slow velocity decrease reaching a

minima which is followed by a very fast velocity

increase close to the lid.

Numeric Level

Symbolic Lifting

IF

Predicates

Physical Object


Left wall

Bottom

Lid

Right wall

FE Core Conceptual Model


FE Elements


Cell

Patch

boundary

Patch

boundary

Mesh

Block

FE Elements


Physics/Material Properties


Kinematic

viscosity

Dynamic viscosity

(µ)/Fluid density(ρ)

Fluid Solid

Velocity

Velocity

Pressure

Navier-Stokes

Equation

Physics/Material Properties



are Connected

are Connected

are Connected

are Connected

Box

Wall

Is Part of

Fluid

is Inside

Topology

Topology


Simulation Results


FE Core Conceptual Model


The SIFEM Conceptual Model


Data View


Data Selection

y

0.05

Feature Extraction (Symbolic Lifting)


Minima=(0.055,-0.20)

fast increase

slow decrease

followed by

(avg first derivative > 35)

velocity starts

at 0 at the

bottom

maximum

velocity is 0.93

at the lid

Data Interpretation Statements


:DataView1 :hasDimensionY :VelocityX .

:DataView1 :hasDimensionX :DistanceFromTheCavityBase .

:DataView1 :x0 “0.0"^^xsd:double .

:DataView1 :y0 “0.0"^^xsd:double .

:DataView1 :hasMinimumX “-0.055"^^xsd:double .

:DataView1 :hasMinimumY “-0.20"^^xsd:double .

:DataView1 :hasFeature :PositiveSecondDerivative .

:DataView1 :hasBehaviour :BehaviourRegion1 .

:DataView1 :hasBehaviour :BehaviourRegion2 .

:BehaviourRegion1 :avgFirstDerivative “-3.63"^^xsd:double .

:BehaviourRegion1 :hasFeature EndRegion .

:BehaviourRegion1 :hasFeature :Decreases .

:BehaviourRegion1 :hasFeature :DecreasesSlowly .

:BehaviourRegion2 :avgFirstDerivative “33.35"^^xsd:double .

:BehaviourRegion2 :hasFeature EndRegion .

:BehaviourRegion2 :hasFeature :Increases .

:BehaviourRegion2 :hasFeature :IncreasesFast .

:BehaviourRegion1 :isFollowedBy :BehaviourRegion1 .

: LidSimulation :hasInterpretation :ValidVelocityBehaviour .

Data Analysis Rule

Data Analysis Rules


CONSTRUCT

{ :LidSimulation sif: hasInterpretation :ValidVelocityBehaviour }

WHERE {

?dataview rdf:type dao:DataView .

?dataview dao:hasFeature ?x .

...

}

IF( minima(velocity) is negative AND

decreases very slowly(velocity) AND

increases very fast (velocity) )

VALID VELOCITY BEHAVIOUR

SPARQL Rule

Data Analysis Workflow


FE Data Analysis



:DataView1 :hasDimensionY :BasilarMembraneMagnitude .

:DataView1 :hasDimensionX :DistanceFromTheCochleaBasis .

:DataView1 :hasFeature :isSingleWave .

:DataView1 :hasMaximumAmplitude “0.0031 "^^xsd:double.

:DataView1 :hasMaximumY “0.0020 e^-6 "^^xsd:double .

:DataView1 :hasMaximumX “14"^^xsd:double .

:DataView1 :hasMinimumY “-0.0011 e^-6 "^^xsd:double .

:DataView1 :hasMinimumX “17"^^xsd:double .

Architecture


Infrastructure/Data Attribution


Future Directions

• Finalization of the semantic infrastructure

• Explore heuristics for the automatic exploration of the

parameter space

• Replicate an existing scientific discovery

• Engage users


Demonstration (Video)


Take-away message

• Contemporary science demands new infrastructures to

scale scientific discovery in a complex knowledge

environment.

• In SIFEM we aim at experimenting with new

infrastructures based on Semantic Web standards to

support better: • Resource Coordination

• Reproducibility

• Efficiency & Automation

• Infrastructure/Data Attribution

• This institute can be a protagonist in this process.


must!

how semantic technologies can help to cure hearing loss?

Software

data analytics slide

reproducibility insight

fluid insight centre

floridi insight centre

middle insight centre

mathematics insight

experimental data

fe elements insight