semantic web vision and its relevance to open digital data for mgi
DESCRIPTION
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_WorkshopTRANSCRIPT
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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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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 ….
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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
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From simple ontologies
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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
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to complex ontologies
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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
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Ontology Development and Alignment @Kno.e.sis
life sciences and health care:PEOSSN
PhylOntMaterial and Biomaterial
MOBMO……
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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
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A little bit about semantic metadata extractions and annotations
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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
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Automatic Semantic Metadata Extraction/Annotation of Textual Data
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Providing Physician Contextually Relevant Information in EMR: Extraction and Annotation using an ontology
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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
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Active Semantic Electronic Medical Record
Active Semantic Electronic Medical Record, 2006, ISWC 2006
Example of Real World System: 1
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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