KENI Pilot

Status Report

Contents

1. Intro and Overview – 3 min. - Joe

2. KENI Architecture – 5 min. – Joe

3. Ontology – 5 min. – Nathan

4. Quant Modeling – 5 min. – Krishna

5. Pilot Model – 7 min – Joe and Krishna

6. Q&A – 5 min.

KENI Pilot Overview

• Purpose - leverage successful representation and computation

methods from an array of disciplines, integrating them into an

interdisciplinary informatics intelligence solution

• Organization - three core components:

– KENI Architecture

– Ontology

– Quant Modeling

• Pilot team - Jessica Adamick (project manager),

Nathan Baker, Brian Davis, Joe Glick (pilot lead), Liz

Hahn-Dantona, Neil Jacobson, Fred Klaessig, Phil

Lippel, Krishna Rajan and Dennis Thomas

Differentiation - Comparison of KENI Initiative with

Wolfram Alpha, Large Knowledge Collider and IBM Watson

IBM Watson KENI - CCR LarKC Wolfram Alpha

Computes Answer Relevance Selected process

outputs

Answer

Approach - Statistical

match

- Computing

power (85,000

watts)

- Contextual parsing

- Concept

quantification

- Relationship

discovery

- Massive,

distributed and

incomplete

reasoning lab

- Linguistic parsing

- Curating

computable

knowledge

User

Systems Custodian

Systems

KENI Engine

Data Sources

Minimum

Information

Requirements

Quant

Formalisms

KENI Components Overview

KENI Architecture Ontology Quant Modeling

Lead Joe Glick Nathan Baker Krishna Rajan

Inputs - existing models

- pilot content

sources

- pilot data & rules

- literature

abstracts

- ontologies

- taxonomies

- candidate data

- computation

methods &

theories

Deliverables - Prototype model

- Computable

Context

Representation

(CCR)

- Sample

content

- Subsumption

architecture

- Sample data,

rules

- Computational

use cases

KENI Architecture - Status

• Initial Content– NPO

– Internano Taxonomy

– ISO Nano Terminology

– USPTO Class 977 Taxonomy & Abstracts

– caNanoLab Publications Abstracts

– (Krishna’s Data)

• Initial Prototype Functionality– Dynamic multi-factor analysis

– Discovery of relationships and common factors

– Multidimensional quantitative exploration

KENI Architecture - Future

• Next Stage Content– NIH Thesaurus

– Biomedical Knowledge Bases

– Materials Science Knowledge Bases

• Computational Approaches:

XXScenario Automation

XXX

Conceptual

Rationalization

XXXCognitive Architectures

XXXXXInteraction Simulation

XXXXRelevance Inference

XXRule Inference

XXXNeural Modeling

UncertaintyComplexitySilosOpaquenessChaosMitigation Strategies

XXScenario Automation

XXX

Conceptual

Rationalization

XXXCognitive Architectures

XXXXXInteraction Simulation

XXXXRelevance Inference

XXRule Inference

XXXNeural Modeling

UncertaintyComplexitySilosOpaquenessChaosMitigation Strategies

KENI project

Meta-ontology subgroup

Jessica Adamick, Nathan Baker, Brian Davis, Joe Glick, Liz Hahn-

Dantona, Fred Klaessig, Phil Lippel, Dennis Thomas

Long-term objectives

• Develop a “consistent” vocabulary for

nanotechnology across its numerous

domains

• Provide structure for integrating existing

terminologies

• Support machine learning, semantic

search, and related informatics

applications

Aim 1: Inventory vocabularies• Collect, identify, and describe the different

taxonomies/terminologies/etc. that are currently available for Nanotechnology

• Possible terminologies– NCImt (NPO, NCIt, HUGO, ChEBI)

– InterNano NanoManufacturing taxonomy

– USPTO Class 977 hierarchical terminology (taxonomy-ish)

– Standards (ISO TC229, ASTM E56, OECD, IEEE)• What is our relationship to these?

• Who are our advocates?

– Other vocabularies?

• How do we deal with proprietary information in this domain (standards copyright, materials genome, etc.)?– Identification of proprietary info through KENI

– Discriminate between things that are business-sensitive and things that are not free

Aim 2: Describe use cases• Describing the use cases for Nanotechnology ontology in the

KENI project (and beyond)

• Identify communities– What are the primary applications and objectives for each

community?• Develop prioritized requirements and use cases

– Variables• Roles (Researcher, program manager, policy maker, infrastructure

provider, clinician, student, worker, …)

• Environments (Regulatory, manufacturing, safety, clinical, research, …)

– Your feedback is needed

• Identify use cases and requirements– Search (semantic capability, resolving synonomy problems,

searching across resources)

– Machine learning (descriptors for QSAR-like studies)

– Annotation (meta-data for deposited information, nano-TAB, journal articles, etc.)

– Reasoning (logical and probabilistic inference)

– Others?

Aim 3: Develop plan of action

• Develop a plan of action for the “meta-ontology” that “combines” the most relevant terminologies

• Terminology alignment/comparison– Semi-automatic

• NLP-based probabilistic approaches

• Ontology-based logical alignment approaches

– Human curation

– How should the output be presented to end-users?

• Deployment– Versioning

– Description language

– Integration with KENI infrastructure/architecture

• Application– What are the first use-case-based demonstrations?

KENI project

Quant Modeling subgroup

Jessica Adamick, Joe Glick,

Phil Lippel, Krishna Rajan

Targeted

Property(s)

“materials genes”

Challenge:

To construct Robust Correlations

between materials properties to features/ characteristics

Methods:

Data Manifold Representations

Dimensionality Reduction

Machine/Statistical Learning

Uncertainty Quantification

Mapping Materials Discovery= F ( x1 , x2 , x3 , x4 , x5 , x6 , x7 , x8 …)

Materials Genome Mapping

Krishna Rajan

Pilot ModelMaterials Science “Cartography

Initial KENI Prototype

• Reminder: we are prototyping the design

and functionality of the KENI engine, not

interfaces for end users, which is the

responsibility of implementation owners

• The KENI platform is a repository for the

integration and rationalization of multi-

disciplinary knowledge and methods for

nanoinformatics research and discovery

Sample data

Prototype Materials Genome Map

Targeted

Property(s)

“materials genes”

Structure

Assumptions

Context Uncertainty, etc.Predictors,

Provenance

Quant

Method

Compound Property

Targeted

Structure(s)

Site

Prototype Demo

Q & A