This document discusses Kno.e.sis' projects on a federated semantic services platform and material database knowledge discovery for material sciences. It proposes a federated architecture with provenance and access control to realize open digital data sharing while protecting private data. The architecture uses semantic mappings and query processing across distributed public, shared, and private material databases. Provenance metadata captured during experiments can improve reproducibility and trust in material products. Flexible access control policies allow custom sharing of semantic data at different granularities with public communities or collaborators.

1. Federated Architecture with Provenance and Access Control
to realize Open Digital Data for MGI
Amit Sheth and the team
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH-45435

2. Kno.e.sis’ MGI related projects
• Federated Semantic Services Platform for Material
Sciences (funded via AFRL/Rome)
• Materials Database Knowledge Discovery and Data
Mining (funded from AFRL/RX)
Faculty: A. Sheth (PI), K. Thirunarayan (coPI), R.
Srinivasan (coPI), Clare Paul (expert)
Students: K. Gunaratna, M. Panahiazar, S.
Lalithsena, V. Nguyen, N. Bryant, A. Shiveley, N.
Jaykumar
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3. 3

4. Databases
Single
Access
Personal desktops
Lab notebooks
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5. Public-Private Data Sharing
• Enhance publicly available datasets while
retaining intellectual property data for businesses
Private data and metadata
(eg. ongoing experimental processes, intellectual property data)
Selectively shared data and metadata
(eg. with ongoing collaborators, licensed data)
Public data and metadata
(released products, material specifications)
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6. Federated Architecture
1. User
Authentication
2. Federated Semantic
Query Processor
3. Semantics
Mappings
Federal Endpoint
Private
Shared
Public
AC
Processor
Semantic
Query
Processor
Research Lab A
Private
Shared
Public
AC
Processor
Semantic
Query
Processor
Industry Lab B
Private
Shared
Public
AC
Processor
Semantic
Query
Processor
Organization C
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7. 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)

8. Provenance Metadata
• Explains the origins of an artifact, such as
– How was it created?
– Who created it?
– When was it created?
• Example: for a given material X
– Which processes and properties involved?
– Input and output values of those processes?
– Which research/engineering team performed the
experiments?

9. Why Data Provenance?
•
•
•
•
•
•
Verification
Reproducibility
Trust
Testing
Quality
…

10. Product – Process – Product
Output
Input
Processes
Capturing provenance: Sufficient + Accurate
=> Reproduce the same output

11. A Unified Provenance Framework
• Capturing domain-specific provenance
– in addition to the W3C PROV ontology
• Representing in standard RDF
• Query engine for processing provenance queries
• Operators for comparing artifacts’ provenance

12. Can we choose any part of our
Semantic Web data
to share with public community,
or with selective collaborators ?

13. Semantic Web Data
Subject
Predicate
Object
A triple is in the format (Subject, Predicate, Object)
An RDF Dataset is a set of triples

14. Linked Data Story So Far?
Non-open data?
Not there yet!

15. Can we choose any part of our
Semantic Web data
to share with public community,
or with selective collaborators ?

16. 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 pieces to
be shared at anytime

17. Can we choose any part of our
Semantic Web data
to share with public community,
or with selective collaborators ?

18. Federal
Endpoint
User X of either
Public group or Collaborators
Manager Y
of component A
1. Query Rewriting
2. AC-embeded Query Execution
AC
Processes
Creating
Resources
Granting
Permissions
Local Component A
Inferring
Permissions

19. Various Policies
•
•
•
•
Role-based Access Control (RBAC)
Mandatory Access Control (MAC)
Attribute-based Access Control (ABAC)
Discretionary Access Control (DAC)
1. Which policy? Depend on the organization’s
needs!
2. Our AC mechanism can be extended to
support any of these policies

20. Summary
• Semantic Federated Architecture enables us to
–
–
–
–
Enhance the open data access
Protect the confidential information
Improve the communication between collaborating teams
Support the reproducibility of material products with
confidence and trust
– Utilize the power of Semantic Web standards and
technologies to do so more easily, effectively and flexibly

21. Kno.e.sis
Thank you, and please visit us at
http://knoesis.org/
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
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