Semantic Web vision and its relevance
to Open Digital Data for MGI
Amit Sheth
Kno.e.sis – Ohio Center of Excellence in Kno...
Material Genome Initiative – White House (White
Paper)

A data exchange system that will allow researchers to
index, searc...
National Research Council. (2008). Integrated
Computational Materials
Engineering. Washington, DC: The National
Academies ...
Ward CH: Integrating Materials and Manufacturing
Innovation: a new forum for the exchange of
information to integrate
mate...
How to integrate well? From Syntax to Semantics

5
The Semantic Web vision: 1999-2001
• TBL used in his 1999 “Weaving the Web” book with focus on
metadata about Web document...
Semantics & Semantic Web in 1999-2002

7
Taalee Semantic/Faceted Search & Browsing
(1999-2001)
Targeted e-shopping/e-commerce

BLENDED BROWSING & QUERYING

ATTRIBU...
Semantic Search/Browsing/Directory
(2001- …)
Links to news on companies that
compete against Commerce One

Crucial news on...
1
2
3
of
Semantic Web
1
• Ontology: Agreement with a common
vocabulary/nomenclature, conceptual models and
domain Knowledge
• Schema + Knowledge...
2
• Semantic Annotation (Metadata Extraction):
Associating meaning with data, or labeling data so
it is more meaningful to...
3
• Reasoning/Computation: semantics enabled
search, integration, answering complex
queries, connections and analyses (pat...
From simple ontologies
Drug Ontology Hierarchy
(showing is-a relationships)
non_drug_
reactant

interaction_
property

formulary_
property

formu...
to complex ontologies
N-Glycosylation metabolic pathway

N-glycan_beta_GlcNAc_9

GNT-I
attaches GlcNAc at position 2
N-acetyl-glucosaminyl_trans...
Ontology Development and
Alignment @Kno.e.sis
life sciences and health care:
PEO
SSN
PhylOnt
Material and Biomaterial
MO
B...
Semantic Web standards @ W3C
• Semantic Web is built in a layered manner
• Not everybody needs all the layers
…
Queries: S...
Material Ontology (MO)
High level hierarchy in MO ontology
including
Geometry, Materials, Parameters, Pe
rformance, Proces...
Material Ontology (MO)
Control and Sensor parameters in MO
Ontology

24
Material Ontology (MO)
Object properties in MO Ontology

25
BioMaterial Ontology (BMO)
Hierarchy in BMO ontology including
BioMaterial
type, Category, Measurement, Proces
s, Property...
Ontology Development
Classes with the annotations

Annotation:
descriptions, exa
mple, creator, etc

27
A little bit about semantic
metadata extractions and
annotations
Extraction for Metadata Creation

WWW, Enterprise
Repositories

Nexis
UPI
AP
Feeds/
Documents

Digital Videos

...

...

D...
Automatic Semantic Metadata Extraction/
Annotation of Textual Data
Providing Physician Contextually Relevant Information in
EMR: Extraction and Annotation using an ontology
Semantic
Models

Relationship Web
Patterns / Inference / Reasoning

Meta data /
Semantic
Annotations

Search
Integration
A...
Example of Real World System: 1

Active Semantic Electronic
Medical Record

Active Semantic Electronic Medical Record, 200...
Example of Real World System 2

Ontological Approach to
Assessing Intelligence
Analyst Need-to-Know

An Ontological Approa...
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Semantic Web vision and its relevance to Open Digital Data for MGI

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Talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013.

workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop

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  • Taalee (subsequently Voquette and Semagix) was founded in 1999 as an Audio/Video Web Search Company (focus on A/V mainly for scalability and market focus reasons, servicename: MediaAnywhere). Domain models/ontologies were created in major areas (many more than what you can find on Bing in 2011) and automatically populated to build knowledge bases (populated ontologies or WorldModel) from a variety of structured and semistructured sources, and periodically kept up to date. This was than used for semantic annotation/metadata extraction to drive semantic search, browsing, etc applications over data crawled from Web sites.
  • Mediaanywhere’s search snapshot.
  • A domain-specific ontology for PhylogenyanalysisSystematic ontology design processHighly detailed and strongly connected ontologyComprehensive evaluation framework
  • Materials Ontology Kernel (MO) we have developed so far includes classes such as Geometry, Materials, Parameters, Performance, Process Constituent, Processing, Structure and Type. It contains more than 300 classes at this stage, including 35 object properties defined by experts  and material types from MatWeb
  • Biomaterials Ontology Kernel (BMO) we imported all bindings as molecular functions from GO ontology (see the appendix)we constructed high level taxonomy for biomaterial concepts such as Biomaterial_Type, Category, Measurements, Process, Property and Structure. It contains more than 100 classes plus bindings as molecular function which amounts to more than 1000 binding terms  imported from GO ontology
  • Semantic Web vision and its relevance to Open Digital Data for MGI

    1. 1. Semantic Web vision and its relevance to Open Digital Data for MGI Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, OH-45435 1
    2. 2. Material Genome Initiative – White House (White Paper) 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 2
    3. 3. National Research Council. (2008). Integrated Computational Materials Engineering. Washington, DC: The National Academies Press 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 3
    4. 4. 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 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 4
    5. 5. How to integrate well? From Syntax to Semantics 5
    6. 6. 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 6
    7. 7. Semantics & Semantic Web in 1999-2002 7
    8. 8. Taalee Semantic/Faceted Search & Browsing (1999-2001) Targeted e-shopping/e-commerce BLENDED BROWSING & QUERYING ATTRIBUTE & KEYWORD QUERYING assets access SEMANTIC BROWSING uniform view of worldwide distributed assets of similar type Taalee Semantic Search ….
    9. 9. Semantic Search/Browsing/Directory (2001- …) Links to news on companies that compete against Commerce One Crucial news on Commerce One’s competitors (Ariba) can Links to news on companieseasily and be accessed Commerce One competes against automatically (To view news on Ariba, click on the link Search for company ‘Commerce One’ for Ariba)
    10. 10. 1 2 3 of Semantic Web
    11. 11. 1 • 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
    12. 12. 2 • 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.
    13. 13. 3 • Reasoning/Computation: semantics enabled search, integration, answering complex queries, connections and analyses (paths, sub graphs), pattern finding, mining, hypothesis validation, discovery, visualization
    14. 14. From simple ontologies
    15. 15. Drug Ontology Hierarchy (showing is-a relationships) non_drug_ reactant interaction_ property formulary_ property formulary indication monograph _ix_class prescription _drug_ property cpnum_ group property indication_ property brandname_ individual brandname_ undeclared prescription _drug_ brand_name brandname_ composite generic_ composite prescription _drug prescription _drug_ generic generic_ individual owl:thing interaction interaction_ with_prescri ption_drug interaction_ with_non_ drug_reactant interaction_ with_mono graph_ix_cl ass
    16. 16. to complex ontologies
    17. 17. N-Glycosylation metabolic pathway N-glycan_beta_GlcNAc_9 GNT-I attaches GlcNAc at position 2 N-acetyl-glucosaminyl_transferase_V N-glycan_alpha_man_4 GNT-V attaches GlcNAc at position 6 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 UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021
    18. 18. Ontology Development and Alignment @Kno.e.sis life sciences and health care: PEO SSN PhylOnt Material and Biomaterial MO BMO ……
    19. 19. Semantic Web standards @ W3C • Semantic Web is built in a layered manner • Not everybody needs all the layers … Queries: SPARQL, Rules: RIF Semantic Web Rich ontologies: OWL Simple data models & taxonomies: RDF Schema Uniform metamodel: RDF + URI Encoding structure: XML Encoding characters : Unicode
    20. 20. Material Ontology (MO) High level hierarchy in MO ontology including Geometry, Materials, Parameters, Pe rformance, Process Constituent, Processing, Structure and Type 23
    21. 21. Material Ontology (MO) Control and Sensor parameters in MO Ontology 24
    22. 22. Material Ontology (MO) Object properties in MO Ontology 25
    23. 23. BioMaterial Ontology (BMO) Hierarchy in BMO ontology including BioMaterial type, Category, Measurement, Proces s, Property, Structure and molecular function 26
    24. 24. Ontology Development Classes with the annotations Annotation: descriptions, exa mple, creator, etc 27
    25. 25. A little bit about semantic metadata extractions and annotations
    26. 26. Extraction for Metadata Creation WWW, Enterprise Repositories Nexis UPI AP Feeds/ Documents Digital Videos ... ... Data Stores Digital Maps ... Digital Images Create/extract as much (semantics) metadata automatically as possible; Use ontlogies to improve and enhance extraction Digital Audios EXTRACTORS METADATA
    27. 27. Automatic Semantic Metadata Extraction/ Annotation of Textual Data
    28. 28. Providing Physician Contextually Relevant Information in EMR: Extraction and Annotation using an ontology
    29. 29. Semantic Models Relationship Web Patterns / Inference / Reasoning Meta data / Semantic Annotations Search Integration Analysis Discovery Question Answering Situational Awareness Metadata Extraction RDB Text Structured and Semistructured Data Multimedia Content and Web Data Sensor Data
    30. 30. Example of Real World System: 1 Active Semantic Electronic Medical Record Active Semantic Electronic Medical Record, 2006, ISWC 2006
    31. 31. Example of Real World System 2 Ontological Approach to Assessing Intelligence Analyst Need-to-Know An Ontological Approach to the Document Access Problem of Insider Threat, 2005

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