Presenting the SPARQL Compatible seRvice laYer (SCRY) at the Diversity++ workshop of the International Semantic Web Conference 2015. See also the paper published in its proceedings.

1. SCRY
SPARQL Compatible Service Layer
Diversity++ Workshop @ ISWC 2015
Bas Stringer, VU Amsterdam
b.stringer@vu.nl

2. 37B triples > 600K RDF dumps > 640 SPARQL endpoints
Source:
http://lod-cloud.net/

3. Different Users → Different Needs
● Domain-specific...
– Data
– Applications
– Problems (Challenges!)
– Solutions
● No “One Size Fits All” fix!
But why not?

4. Data != Knowledge
● Semantic Web → data
● Answers ← knowledge
● Typically can't be precomputed
– Must be done at query time!
● Make “implicit knowledge” explicit
– Qualitative reasoning: OWL
– Quantitative reasoning: Depends!
?
Reasoning

5. Quantitative Reasoning
● “I have people's heights and weights. BMI?”
● “I have people's BMIs. Standard deviation?”
● “Is there a correlation between geospatial
location and BMI?”
● “Find genes with mutations which frequently co-
occur with obesity. Are there drugs which target
these genes?”
Do other traits correlate?
What about similar genes?

6. Quantitative Reasoning
● Current approaches:
– Built-in SPARQL functions
– Pre- and/or post-scripting
– Extensible Value Testing
– Linked Data APIs
– Endpoint customization

7. SCRY
● SPARQL compatible service layer
– Lightweight, federation-oriented endpoint
– Graph patterns encode service calls
– Input/output = RDF
– Graph generated on demand

8. Easy to Customize!
● SCRY is written in Python
– Implement services in Python
– … or call them from commandline
– … or as a webservice!
From paper “To SCRY Linked Data: Extending SPARQL the Easy Way”

9. Example 1: Standard Deviation
Demo data:

10. Bind a comma-separated list
of the ages and lengths
Federate a query to SCRY
Every unique combination of
inputs and outputs get their
own separate subgraph
You can call the same service
with different inputs, and different
services with the same inputs!
Use the output back at the
primary endpoint / query
Which authors have an age and
length within one SD of the mean?

11. Example 2: BLAST
Protein Structure
Function
Disease
ExpressionHomolog

12. Which tissues express Hemoglobin β?
How many homologs are coexpressed there?
● Get protein identifier
● Get homologs
● Get expression data
● Count!
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13. Pros Cons
● Flexible
– Easily customized
● Reusable
– Share services
● Local
– Private data
– Private resources
● Interoperable
– Query federation
● Query federation
– No direct data access
– Network latency
– Authorization

14. Outlook
● Develop a user community
– Community-managed service repository
● Security and authorization features
– Enable public SCRY orbs
● Solve current problems in bioinformatics
– E.g. queries involving homology!
● Find use cases in other fields of science!
– (Co-)develop required & desired services
– Get in touch with interested collaborators

15. Thank you for your time!
To SCRY Linked Data: Extending SPARQL the Easy Way
Diversity++ Workshop @ ISWC 2015