Semantic Technologies for Big Science and Astrophysics

Invited presentation: EarthCube Solar-Terrestrial End-User Workshop

NJIT, Newark NJ, August 13-15, 2014

Amit Sheth, T. K. PrasadKno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing

Wright State University, Dayton, OH - 45435

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Astrophysics

Lots of data

Complex

Heterogeneous

http://en.wikipedia.org/wiki/Astrophysics#mediaviewer/File:NGC_4414_%28NASA-med%29.jpg

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• How can we handle this vast, heterogeneous,

and complex data space?• Focus on complexity rather than raw processing:

integration, collaboration, reuse

Challenge

Can Semantic (Web) technologies ease the challenges and empower the scientists?

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The Semantic Web vision: 1999-2001

• Sir Tim Berners Lee, in his 1999 “Weaving the Web” book, emphasized the significance of metadata about Web documents.

• Well known May 2001 article presented an agent and an AI based vision for “next generation of the World Wide Web” with 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. Had commercial applications starting 2000, patent awarded in 2001.

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3

of

Semantic Web

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• Agreement and Knowledge: Agreement about a common vocabulary/nomenclature, conceptual models and domain knowledge, ontology

– Codified as Schema + Knowledge Base. – Agreement is what enables interoperability.– Formal machine processable description is what

leads to automation.– Manual, semi-automated, automated creation of

ontologies

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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.

– Manual– Semi-automatic (automatic with human

verification)– Automatic

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• Reasoning/Computation, Applications: – Semantics enabled search, browsing – Data integration, collaboration– Visualization– Analyses including pattern discovery, mining, hypothesis

validation – Answering complex queries, making connections (paths,

sub graphs), supporting discovery

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How to integrate well? From Syntax to Semantics

SSNOntology

2 Interpreted data(deductive)[in OWL] e.g., threshold

1 Annotated Data[in RDF]e.g., label

0 Raw Data[in TEXT]e.g., number

Using Semantics to Climb Levels of Abstraction: an example

3 Interpreted data (abductive)[in OWL]e.g., diagnosis

Intellego

“150”

Systolic blood pressure of 150 mmHg

ElevatedBlood

Pressure

Hyperthyroidism

less

use

ful …

…

mor

e us

eful

……

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Semantic Web technologies – in practice

● Ontologies to capture domain knowledge (sometimes taxonomy/nomenclature is good enough)

● Languages to represent/capture domain knowledge and data - OWL, RDF/RDFS.

● Data sharing and publishing online (e.g., LOD).● Annotation, semantic search, semantic browsing● Provenance,…

Widely used in biomedicine; quite a few applications in healthcare, growing use and explorations in geosciences and more…

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In this talk, I will review/borrow from

• ScienceWISE at EPFL which uses semantic technology to serve Physicists including Astrophysicists: shared vocabulary, annotation, browsing for related concepts

• Semantic (web) technologies for health care and life sciences encompassing collaborative research, prototypes, open source tools and ontologies, deployed applications, commercialization,…

• MaterialWays: Our project in Materials Genome Initiatives …

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“Ontology” in physics domain – ScienseWISE

● ScienceWISE WISE - Web based Interactive Semantic Environment

● An interactive and crowdsourced tool to capture knowledge from scientists’ daily routine work.

● Core consists of a community built ontology.● Literature gets annotated and bookmarked using

the ontology.

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ontology

annotation

bookmarking & recommendations

http://sciencewise.info/

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Value Proposition

Associating machine-processable semantics with scientific, engineering data and

documents can help overcome challenges associated with data discovery, integration

and interoperability caused by data heterogeneity.

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Benefits of using semantics for Astrophysicists (and other sciences)

• Challenges– Massive volume– Heterogeneity (i.e., from many sources, format/structure, text,

images).– Interoperability and sharing data– Provenance and Access Control.

• Need techniques beyond ScienceWISE– Interested in data beyond scientific publications– Data sharing (and credit/data citation for data sharing)– Provenance and Access control– A framework to capture, search, and discover astrophysical

data

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Nature of Data and DocumentsRelational/Tabular Data

XML document

Image

Technical Specs

Irregular Tables

Publications

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Granularity of Semantics and Applications: Examples

• Synonyms– Chemistry, Chemical Composition, Chemical Analysis, ...– Bend Test, Bending, ...– Delivery Condition, Process/Surface Finish, Temper, "as received by

purchaser", ...

• Coreference vs broadening/narrowing– Tubing vs welded tubing vs flash-welded part

• Capturing characteristic-value pairs– Recognize and Normalize: “0.1 inch and under in nominal thickness”

is translated to “Thickness <= 0.1 in”.– Glean elided characteristic: controlled term “solution heat treated”

implies the characteristic “heat treat type”.

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Granularity of Semantics and Associated Applications

• Lightweight semantics: File and document-level annotation to enable discovery and sharing

• Richer semantics: Data-level annotation and extraction for semantic search and summarization

• Fine-grained semantics: Data integration and interoperability.

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Using Semantic Web Technologies

Machine-processable semantics achieved by addressing

• Syntactic Heterogeneity: Using XML syntax and RDF datamodel (labelled graph structure)

• Semantic Heterogeneity: – Using “common” controlled vocabularies, taxonomies

and ontologies – Using federated data sources, exchanges, querying,

and services

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Ingredients for Semantics-based Cyber Infrastructure

• Use of community-ratified controlled vocabularies and lightweight ontologies (upper-level, hierarchies)

• Semi-automatic annotation of data and documents

• Support for provenance and access control

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A proposed “light-weight semantics” approach

(for highly distributed community, low start up time, long tail science)…

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Our applications in Materials Genome Initiative

Materialways (our project related to Material Genomics Initiative):http://wiki.knoesis.org/index.php/MaterialWays

Matvocab home page

Search and discovery

Annotate documents

Visualize the knowledge base

Query vocabulary

View, edit, and add

Create processassertions

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Search & Discovery

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Annotate, search, and track provenance

• Vocabulary is used to annotate documents.

• Annotated documents can be indexed.

• Documents can be integrated reliably based on common terms of interest and provenance information.

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Annotate documents using standard vocabulary

Create process assertions (OnCET)

• Add information about inputs to and outputs of a process as assertions in triple form using standard vocabulary.

• Add assertions about materials domain knowledge using vocabulary terms and relationship among them, e.g., about process control parameters and performance characteristics.

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• Explains the origin of an artifact, such as– How was it created?– Who created it?– When was it created?

• Example: for a given material X– Which processes are involved in making the material and

what are the relevant performance properties? – What are the inputs, control parameters and outputs of a

process?– Which research/engineering team performed an

experiment?

Provenance Metadata

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Capturing provenance metadata - iExplore

generic PMC prepreg

generic hand lay-up

generic PMC lay-up

generic autoclave cure

generic PMC

subjected to

subjected to

yields

yields

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Vocabulary Provenance

ASM Handbook

MIL Handbook 5

MIL Handbook 17Vocabulary terms

Vocabulary term expressed in RDF and published online (http://knoesis.org/matvocab/A-basis)Wiki-based Crowd-sourcing Vocabulary

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Capturing Vocabulary Provenance - iExplore

Definition

Rights

Source

Vocabulary term

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Our proposal - Astrophysics

• Tagging, annotation, search• Knowledgebase -> Ontology• Provenance – at every data

level• Data access control• Capture process flows• Capture relationships

between concept instances• Visualization of process flows

ScienceWISE - Physics

• Tagging, annotation, search• Ontology ->

Knowledgebase• Provenance

Our approach to help in Astrophysics

• Access control and provenance details at every data level -> handle huge amount of astrophysics data.

• Create relationships between concepts and visualize them in graph format.

• Adding facts or assertion about each concept.

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Data Access

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Personal desktops

Lab notebooks

Databases

Single Access

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Public-Private Data Sharing

• Enhance publicly available datasets while retaining intellectual property data privately for businesses

Private data and metadata(e.g. ongoing experimental processes, intellectual property data)

Selectively shared data and metadata(e.g. with ongoing collaborators, licensed data)

Public data and metadata (e.g., released products, material specifications)

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OEM partner A

Federated Architecture

Private

Shared

Public

Federal Endpoint

1. User Authentication

2. Federated Semantic Query Processor

AC Processor

Semantic Query

Processor

OEM partner B

Private

Shared

Public

AC Processor

SemanticQuery

Processor

OEM supplier C

Private

Shared

Public

AC Processor

Semantic Query

Processor

3. Semantics Mappings

Principles of a Federation

• Each component controls access to its local data independently (local autonomy).

• A query is decomposed to multiple sub-queries, each sub-query is executed at one component.

• Results from sub-queries are combined by the federated query processor (control global access)

Can we choose any part of our Semantic Web data

to share with public community, or with selective collaborators ?

Different levels of granularity

– Individual resources• Example: a material product, a manufacturing process

– Individual triples• Example: properties of a product, or process

– Entire datasets

Enable flexible selection of any data piece to be shared at anytime

Local Component A

Creating Resources

Granting Permissions

Inferring Permissions

AC Processes

Federal Endpoint

2. AC-embedded Query Execution

User X of either Public group or Collaborators

Manager Yof component A

1. Query Rewriting

Various Policies

• Role-based Access Control (RBAC)• Mandatory Access Control (MAC)• Attribute-based Access Control (ABAC)• Discretionary Access Control (DAC)

1. Which policy? Depends on the

organization’s needs!

2. Our AC mechanism can be extended to

support any of these policies.

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Advance capability: semantic browsing

• Example of Scooner: http://wiki.knoesis.org/index.php/Scooner

• Demo:http://knoesis.wright.edu/library/demos/scooner-demo/

Take Away

Use of semantic web technologies

can help overcome challenges associated with

data discovery, integration, and interoperability, caused by data heterogeneity.

Use provenance and access control information

help share/exchange data reliably.

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Thank you, and please visit us at

http://knoesis.org/http://wiki.knoesis.org/index.php/MaterialWays

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled ComputingWright State University, Dayton, Ohio, USA

Special Thanks (MaterialWays team): . Clare Paul (AFRL),Kalpa Gunaratna, Vinh Nguyen, Sarasi Lalithsena, Swapnil Soni. Nitisha Jayakumar,

Siva Cheekula.

Kno.e.sis