Keynote at ESWC 2014, Crete, May 27, 2014

1. Steffen Staab
staab@uni-koblenz.de
1WeST
Web Science & Technologies
University of Koblenz ▪ Landau, Germany
Programming the Semantic Web
Steffen Staab & Team@WeST

2. Steffen Staab
staab@uni-koblenz.de
2WeST
This presentation contains program code,
engineering, speculation and worse.
It may be viewed as offensive by some
viewers.

3. Steffen Staab
staab@uni-koblenz.de
3WeST
Semantic Web: Great History, Still Long Way to Go!
Big
Systems
Practice
Research
influence
Wanted: mainstream adoption!
Target: ProgrammingTarget: Programming

4. Steffen Staab
staab@uni-koblenz.de
4WeST
SEMANTIC WEB PROGRAMMING:
BIRD‘S EYES VIEW

5. Steffen Staab
staab@uni-koblenz.de
5WeST
Semantic Web Programming
Linked Data
SPARQL endpoint
RDF file
Hypothesis:
Semantic Web data is wonderful,
but programming with Semantic
Web has changed too little since
2000 and still is a mess.
We promise
flexibility, but code
still hard to
maintain.
Ontology

6. Steffen Staab
staab@uni-koblenz.de
6WeST
Semantic Web Programming
Linked Data
SPARQL endpoint
RDF file
„Inside“
Data
Mgmt
Eclipse
Visual Studio
....
Ontology

7. Steffen Staab
staab@uni-koblenz.de
7WeST
Semantic Web Impedance Mismatch
„Inside“
 http get
 Dereferencing
 Query execution
 Updating
 Indexing
 Triplification
 Integration
 Streaming
„Inside“
Data
Mgmt
Eclipse
Visual Studio
....
Mind
the
Gap!

8. Steffen Staab
staab@uni-koblenz.de
8WeST
Semantic Web
Linked Data
SPARQL endpoint
RDF file
„Inside“
Data
Mgmt
„Outside“
Data
Mgmt
Eclipse
Visual Studio
....
Ontology

9. Steffen Staab
staab@uni-koblenz.de
9WeST
Semantic Web
„Inside“
Data
Mgmt
„Outside“
Data
Mgmt
Eclipse
Visual Studio
....
„Outside“
 Understanding
by the developer
 „Search + Code“
 „Browse + Code“
 Triple-object mapping
 Code generation
 Process of federation
 LD vs endpoint
 Models of federation
Abstraction layers
that facilitate a
developer‘s life

10. Steffen Staab
staab@uni-koblenz.de
10WeST
Programming with Data: What does it cost?
Ctotal = t*Ctool + d*t*Clearn + s*Cdeu + s*Cmap + n*Ccode
Ctool : Costs for building t many tools, shared; almost free
Clearn: Costs for learning how to use technology per developer d
Cdeu: Costs for data engineering/understanding s sources
Cmap : Costs for mapping data structure for s sources to objects
Ccode : Actual costs for accessing/manipulating data n times
„Inside“Both „Outside“

11. Steffen Staab
staab@uni-koblenz.de
11WeST
SWOT of Semantic Web Programming
Ctotal = t*Ctool + d*t*Clearn + s*Cdeu + s*Cmap + n*Ccode
threat
opportunity
opportunity
weakness
Not a
strength, yet!
strength/
weakness
Strong in flexibility
Somewhat weak in
performance
„Outside“
as good as RelDB is
not good enough!

12. Steffen Staab
staab@uni-koblenz.de
12WeST
Target cost scenario 1: Automated setup
Ctotal
n (as in n*Ccode )
SemWeb coding efforts
RelDB/XML coding efforts
Applies to
small n
Diff.costsfor
learning
Setup
cost
0

13. Steffen Staab
staab@uni-koblenz.de
13WeST
Target cost scenario 2: Manually perfected setup
Ctotal
n (as in n*Ccode )
SemWeb coding efforts
RelDB/XML coding efforts
„Perfect“ object
model shields
developer from
database
ideosyncracies
Setupcosts
0

14. Steffen Staab
staab@uni-koblenz.de
14WeST
Intermediate conclusion
Minimize costs for setup:
Clearn: Costs for learning how to use technology per developer d
Cde: Costs for data engineering/understanding s sources
Cmap : Costs for mapping data structure for s sources to objects
Minimize costs for core programming:
Ccode : Actual costs for accessing/manipulating data n times
Costs for learning and understanding
constitute a threat!
Need to be overcome!

15. Steffen Staab
staab@uni-koblenz.de
15WeST
SEMANTIC WEB PROGRAMMING:
IDENTIFYING SOME PITFALLS

16. Steffen Staab
staab@uni-koblenz.de
16WeST
Example scenario: Jamendo
Data about license free music
 ~ 1 Million triples
 classes and predicates
from 18 different ontologies
 FOAF, Tag ontology,
music ontology, …
Simple programming task:
 List for every music artist,
all the records they made

17. Steffen Staab
staab@uni-koblenz.de
17WeST
Software Development Process Overview
data model
design
revised data
model design
data model
prototype
data
queries
final data
model
Creation of
initial data
model
Exploration
of the data
source
Creation of
model in
code
Query design /
implementation
Mapping
of query
results

18. Steffen Staab
staab@uni-koblenz.de
18WeST
Accessing Artists Using Apache Jena

19. Steffen Staab
staab@uni-koblenz.de
19WeST
From artists to songs
Observations
 SPARQL queries are strings
 Results are strings
 Requires good understanding of the data source
RDF Typing is lost

20. Steffen Staab
staab@uni-koblenz.de
20WeST
Related Work on RDF Access
Static Typing
 Errors detected before
execution
 Misspelling discovered
by compiler!
 Anectode: 2nd place
because of misspelt var.
 Static types are form of
documentation
 Less knowledge about
data source required
 Better IDE integration /
autocompletion
Code generation
 Sommer
 Winter
 OntoMDE
Dynamic Typing
 E.g. ActiveRDF
(Oren et al 2007))
 “convention over
configuration”
 dynamic
metaprogramming
allows for slick code
Criticism

21. Steffen Staab
staab@uni-koblenz.de
21WeST
SEMANTIC WEB PROGRAMMING:
LITEQ – OUR OUTSIDE APPROACH

22. Steffen Staab
staab@uni-koblenz.de
22WeST
Programming against Jamendo
c1

23. Steffen Staab
staab@uni-koblenz.de
23WeST
Node Path Query Language Using Autocompletion
Exploration of classes
Exploration of relations
Querying for instances

24. Steffen Staab
staab@uni-koblenz.de
24WeST
Node Path Query Language Using Autocompletion
Exploration of classes
Exploration of relations

25. Steffen Staab
staab@uni-koblenz.de
25WeST
Node Path Query Language: Query Formulation
Exploration of classes
Exploration of relations
Querying for instances
Type
set of mo:MusicArtist
No definition or
declaration needed

26. Steffen Staab
staab@uni-koblenz.de
26WeST
Node Path Query Language for Code Development
Exploration of classes
Exploration of relations
Querying for instances
Developing code with queries
All translated into SPARQL queries at
• Development time
• Type inference at compile time
(but also as part of IDE)
• Querying again at run time
One language to bind them all

27. Steffen Staab
staab@uni-koblenz.de
27WeST
Node Path Query Language for Code Development
Exploration of classes
Exploration of relations
Querying for instances
Developing code with queries
Developing code with new classes
All translated into SPARQL queries at
• Development time
• Run time update
• Persistence!

28. Steffen Staab
staab@uni-koblenz.de
28WeST
NPQL
NPQL (Node Path Query Language)
 Intensional Queries
 Describing RDF classes and properties
for reuse in IDE and in host language metaprogramming
 Extensional Queries
 Class instances and property instances
 Compilation to SPARQL for reuse of existing endpoints
Ongoing discussion
about details of NPQL

29. Steffen Staab
staab@uni-koblenz.de
29WeST
LITEQ
NPQL (Node Path Query Language)
 Intensional Queries
 Extensional Queries
 Compilation to SPARQL
LITEQ (Language Integrated Types, Extensions and Queries)
 Implementation of NPQL as F# Type Provider in Visual Studio
 Autocompletion using NPQL queries
 Automatic typing
of extensional query results
by intensional queries

30. Steffen Staab
staab@uni-koblenz.de
30WeST
Cost savings
Ctotal = t*Ctool + d*t*Clearn + s*Cdeu + s*Cmap + n*Ccode
Ctool : open source
Clearn: not free – though autocompletion reduces cognitive load
Cdeu: not free – understanding the RDF schema from your IDE
Cmap : 0
Ccode : a lot less than for dotNet RDF (Apache Jena?!!)
little bit more than for a fictitious perfect object model

31. Steffen Staab
staab@uni-koblenz.de
31WeST
Halstead metrics for different tasks:
Conventional Semantic Web programming approaches waste
up to 50% of your efforts!

32. Steffen Staab
staab@uni-koblenz.de
32WeST
Speculation 1
Ctotal
n (as in n*Ccode )
SemWeb coding efforts
RelDB/XML coding efforts
Applies to
small n
Diff.costsfor
learning
Speculation 1: Using ontologies and RDF
schemata, we can develop more efficiently
using the right tools!

33. Steffen Staab
staab@uni-koblenz.de
33WeST
Halstead metrics for different tasks:
If someone gives me a perfect RDF-to-OO mapping for free
then I will not care about whether it is RDF or RelDB
underneath!

34. Steffen Staab
staab@uni-koblenz.de
34WeST
Speculation 2
Ctotal
n (as in n*Ccode )
SemWeb coding efforts
RelDB/XML coding efforts
„Perfect“ object
model shields
developer from
database
ideosyncracies
Diff.costsfor
setup
Speculation 2: For large programmes, our
tools need to offer better support to reduce
setup costs!

35. Steffen Staab
staab@uni-koblenz.de
35WeST
Issues
 No schema
 many LOD sources do not have schema

36. Steffen Staab
staab@uni-koblenz.de
36WeST
Issues
 No schema
 many LOD sources do not have schema
 Useless schema
New kinds of (?)
schema induction!

37. Steffen Staab
staab@uni-koblenz.de
37WeST
Issues
 No schema
 many LOD sources do not have schema
 Useless schema
 Property-based schemata
 Cf. Homoceanu et al ISWC 2013
• ask for all movies – not possible by asking for class movie,
but only for different property combinations

38. Steffen Staab
staab@uni-koblenz.de
38WeST
Outlook wrt LITEQ
 Integration of schema induction
 Programming the Semantic Web as a whole
 Federated queries
 link traversal-based queries
 More expressive query language
 Composed data types in tractable DL
 More precise integrated type inference
 (composed) types from RDF data source
 type inference in host language

39. Steffen Staab
staab@uni-koblenz.de
39WeST
CONCLUSION

40. Steffen Staab
staab@uni-koblenz.de
40WeST
Semantic Web: Make Developers More Productive
Linked Data
SPARQL endpoint
RDF file
„Inside“
Data
Mgmt
„Outside“
Data
Mgmt
Eclipse
Visual Studio
....
Ontology

41. Steffen Staab
staab@uni-koblenz.de
41WeST
Semantic
Web
Social Web &
Web Retrieval
Interactive Web &
Human Computing
Web &
Economy
Software &
Services
Web Science & Technologies Team & Research
Computational
Social Science
Thank You!
Ralf
Lämmel
Evelyne
Viegas

42. Steffen Staab
staab@uni-koblenz.de
42WeST
References
 S. Scheglmann, A. Scherp, S. Staab.
Declarative Representation of Programming
Access to Ontologies. In: 9th Extended
Semantic Web Conference (ESWC2012),
Heraklion, Greece, May 27-31, 2012.
 Daniel Oberle. How Ontologies Benefit
Enterprise Applications. Semantic Web
Journal. http://www.semantic-web-
journal.net/content/how-ontologies-benefit-
enterprise-applications-0
 S. Homoceanu, P. Wille, W.-T. Balke.
ProSWIP: Property-Based Data Access for
Semantic Web Interactive Programming. In:
ISWC-2013
 C. Saathoff, S. Scheglmann, S. Schenk.
Winter: Mapping RDF to POJOs revisited.
 E. Oren, R. Delbru, S. Gerke, A. Haller, S.
Decker: ActiveRDF: object-oriented
semantic web programming. WWW 2007:
817-824
 S. Scheglmann, S. Staab, M. Thimm,
G. Gröner. Locking for Concurrent
Transactions on Ontologies. In: 10th
Extended Semantic Web Conference
(ESWC2013), Montpellier, France,
May 26-30, 2013.
 M. Konrath, T. Gottron, S. Staab, A.
Scherp. SchemEX – Efficient
Construction of a Data Catalogue by
Stream-based Indexing of Linked
Data. In: Journal of Web Semantics.
Special issue on Semantic Web
Challenge Winners 2011. Elsevier,
Volume 16, November 2012, pp. 52-
58.
 W. Cook, A. Ibrahim. Integrating
Programming Languages &
Databases: What’s the Problem??
http://citeseerx.ist.psu.edu/viewdoc/do
wnload?doi=10.1.1.66.7169&rep=rep1
&type=pdf