Semantic Web vision and its relevance to Open Digital Data for MGI

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Semantic Web vision and its relevance

to Open Digital Data for MGIAmit Sheth

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing

Wright State University, Dayton, OH-45435

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A data exchange system that will allow researchers to index, search, and compare data must be

implemented to allow greater integration and collaboration. In the discovery stage it is crucial that researchers have access to the largest possible data

set upon which to base their models, in order to provide a more complete picture of a material’s

characteristics. This can be achieved through data transparency and integration

Material Genome Initiative – White House (White Paper)

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Integrating materials computational tools and information with sophisticated computational and

analytical tools already in use in engineering fields… [promises] to shorten the materials development cycle from its current 10-20 years to 2 or 3 years

National Research Council. (2008). Integrated Computational Materials Engineering. 

Washington, DC: The National Academies Press

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Our community is entering an era where individual computational tools and dispersed experimental and

modeling data must be brought together to create integrated toolsets that are made available to

materials, manufacturing, and design engineers to create a Materials Innovation Infrastructure, as called

for through the Materials Genome Initiative

Ward CH: Integrating Materials and Manufacturing Innovation: a new forum for the exchange of

information to integrate materials, manufacturing, and design engineering innovations. Integrating Materials and Manufacturing Innovation 2012

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How to integrate well? From Syntax to Semantics

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The Semantic Web vision: 1999-2001

• TBL used in his 1999 “Weaving the Web” book with focus on metadata about Web documents

• Well known May 2001 article presented an agent and AI based vision for “next generation of the World Wide Web” for Web content amenable to automation

• With Taalee (later Voquette, Semagix) I founded in 1999, I pursued a highly practical realization with semantic search, browsing and analysis products

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Semantics & Semantic Web in 1999-2002

BLENDED BROWSING & QUERYINGBLENDED BROWSING & QUERYING

ATTRIBUTE & KEYWORDQUERYING

ATTRIBUTE & KEYWORDQUERYING

uniform view of worldwide distributed assets of similar type

SEMANTIC BROWSINGSEMANTIC BROWSING

Targeted e-shopping/e-commerce

assets access

Taalee Semantic/Faceted Search & Browsing (1999-2001)

Taalee Semantic Search ….

Search for company ‘Commerce One’

Links to news on companies that compete against Commerce One

Links to news on companies Commerce One competes against

(To view news on Ariba, click on the link for Ariba)

Crucial news on Commerce One’s competitors (Ariba) can

be accessed easily and automatically

Semantic Search/Browsing/Directory (2001- …)

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2

3

of

Semantic Web

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• Ontology: Agreement with a common vocabulary/nomenclature, conceptual models and domain Knowledge

• Schema + Knowledge base • Agreement is what enables interoperability• Formal description - Machine processability is what

leads to automation

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• Semantic Annotation (Metadata Extraction): Associating meaning with data, or labeling data so it is more meaningful to the system and people.

• Can be manual, semi-automatic (automatic with human verification), automatic.

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• Reasoning/Computation: semantics enabled search, integration, answering complex queries, connections and analyses (paths, sub graphs), pattern finding, mining, hypothesis validation, discovery, visualization

From simple ontologies

Drug Ontology Hierarchy (showing is-a relationships)

owl:thing

prescription_drug

_ brand_na

me

brandname_unde

clared

brandname_comp

osite

prescription_drug

monograph_ix_cla

ss

cpnum_ group

prescription_drug

_ property

indication_

property

formulary_

property

non_drug_

reactant

interaction_proper

ty

property

formulary

brandname_indivi

dual

interaction_with_prescriptio

n_drug

interaction

indication

generic_ individua

l

prescription_drug_ generic

generic_ composit

e

interaction_ with_non_ drug_react

ant

interaction_with_monograph_ix_class

to complex ontologies

N-Glycosylation metabolic pathway

GNT-Iattaches GlcNAc at position 2

UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 <=>

UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2

GNT-Vattaches GlcNAc at position 6

UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021

N-acetyl-glucosaminyl_transferase_VN-glycan_beta_GlcNAc_9N-glycan_alpha_man_4

Ontology Development and Alignment @Kno.e.sis

life sciences and health care:PEOSSN

PhylOntMaterial and Biomaterial

MOBMO……

Semantic Web standards @ W3C

• Semantic Web is built in a layered manner• Not everybody needs all the layers

Encoding characters : Unicode

Encoding structure: XML

Uniform metamodel: RDF + URI

Simple data models & taxonomies: RDF Schema

Rich ontologies: OWL

Queries: SPARQL, Rules: RIF

…

Semantic Web

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Material Ontology (MO)

High level hierarchy in MO ontology including Geometry, Materials, Parameters, Performance, Process Constituent, Processing, Structure and Type

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Material Ontology (MO)

Control and Sensor parameters in MO Ontology

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Material Ontology (MO)

Object properties in MO Ontology

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BioMaterial Ontology (BMO)

Hierarchy in BMO ontology including BioMaterial type, Category, Measurement, Process, Property, Structure and molecular function

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Ontology Development

Classes with the annotations Annotation: descriptions,

example, creator, etc

A little bit about semantic metadata extractions and annotations

WWW, EnterpriseRepositories

METADATA

EXTRACTORS

Digital Maps

NexisUPIAP

Feeds/Documents

Digital Audios

Data Stores

Digital Videos

Digital Images. . .

. . . . . .

Create/extract as much (semantics)metadata automatically as possible;

Use ontlogies to improve and enhanceextraction

Extraction for Metadata Creation

Automatic Semantic Metadata Extraction/Annotation of Textual Data

Providing Physician Contextually Relevant Information in EMR: Extraction and Annotation using an ontology

TextMultimedia Content

and Web Data

Metadata Extraction

Patterns / Inference / Reasoning

Semantic Models

Meta data / Semantic Annotations

Relationship Web

SearchIntegrationAnalysisDiscoveryQuestion AnsweringSituational Awareness

Sensor Data

RDB

Structured and Semi-structured Data

Active Semantic Electronic Medical Record

Active Semantic Electronic Medical Record, 2006, ISWC 2006

Example of Real World System: 1

Ontological Approach to Assessing Intelligence Analyst Need-to-Know

An Ontological Approach to the Document Access Problem of Insider Threat, 2005

Example of Real World System 2