The document discusses publishing RDF and SKOS using Java microservices, focusing on frameworks like Jena and RDF4J for handling semantic web data. It outlines key concepts such as linked data, resource identifiers, and various RDF serialization formats, along with the advantages and use cases for triple stores versus traditional databases. Additionally, it highlights Dropwizard's capabilities for building microservices and RESTful applications efficiently.

1. Publishing RDF SKOS
with Java microservices
Fedict – Brussel – jan 2017

2. | p. 2
 Linked Data
 Resource Description Framework
 Triple stores
 Jena and RDF4j
 Dropwizard
Agenda

3. | p. 3
Dropwizard RDF4j
Overview
Jetty
Lucene RDF storeJersey
Freemarker
Slf4j

4. Linked Data

5. | p. 5
 Making the web machine-readable
 Distributed / web
 Challenging for queries
 Data not guaranteed to be available / persistent
 Add meaning to relations / links
Semantic web

6. | p. 6
 Using URI as identifier
 Dereferenceable URI
Identifier

7. Resource Description
Framework

8. | p. 8
 <Subject S> <Predicate relation P> <Object O>
 “Triple”
 S and P are resource identifiers (IRI)
 http://example.com, mailto:john@example.com, …
 urn:example:1234-56789, ...
 O can be:
 Identifier (“link” to something else)
 Literal
– String value with optional language tag
– OR typed value (e.g XSD date, integer...)
RDF Basics

9. | p. 9
 RDF is not a file format
 Although .rdf extension is often used for RDF/XML
 Popular serializations
 N-Triples (.nt): fast and easy
 Turtle (.ttl): human-friendly
 RDF/XML (.rdf): XML-flows
 JSON-LD (.json): web devs
RDF serializations

10. | p. 10
 Based upon RDF Schema
 Somewhat similar to XML Schema
 “Classes” and “properties”
 Can (and should be !) be mixed, reused
 Popular vocabularies
 Dublin Core: generic title, description…
 SKOS: broader / narrower term …
 ROV: registered organizations …
 http://lov.okfn.org/dataset/lov/
Vocabularies

11. | p. 11
 RDF can be generated without triple store
 Less suitable for:
 Very large tabular sets (e.g. RDBMS dumps)
 Tiny sensor data
Notes

12. Jena and RDF4j

13. | p. 13
 Both great Java open source frameworks
 Reading/writing/converting RDF, Triple stores ...
 Apache Jena
 https://jena.apache.org/
 Better performance / more scalable ?
 Eclipse RDF4j (Sesame)
 http://rdf4j.org/
 Better architecture (“Sails”) ?
Jena vs RDF4j

14. | p. 14
 Embedded store / standalone server
 100 - 150 mln triples
 No out-of-the-box HA / replication
 Probably not needed for publishing smaller sets
 Running multiple “shared nothing” ?
 Bonus: “Sail” abstraction
 Switch to GraphDB, Blazegraph with minor changes
Why (not) RDF4j as data store

15. Triple stores

16. | p. 16
 TS optimized for storing triples
 TS often lack fine-grained checks
 Few checks for data types, “non-null”
 Commercial stores like StarDog offer more options
 Work in progress: https://www.w3.org/TR/shacl/
 Full text search often handled by Lucene
 Often product-specific extension
 Queries and updates with SPARQL (SQL-alike)
 And / or custom api, faster but less portable
Triple store vs RDBMS

17. | p. 17
 Small / medium sets
 Apache Jena store (part of framework)
 Eclipse RDF4j store (part of framework)
 Larger sets
 Blazegraph (GPU acceleration in comm.version)
 OntoText GraphDB (free demo)
 Oracle Spatial and Graph
 Virtuoso (hybrid XML / RDBMS / TS)
Popular stores

18. | p. 18
 SPARQL endpoints
 Advanced queries
 Heavy load on server side
 Linked Data Fragments
 Very basic queries
 Shifting workload to client
 More network traffic
 http://linkeddatafragments.org/concept/
Distributed queries

19. Dropwizard

20. | p. 20
 Mixing REST / SOA / Unix philosophy
 Do 1 thing and do it well
 Back-end
 Also in Java
 Traditional Java EE to complex for small apps
 Pippo, RH Wildfly Swarm, Jooby, Ninja, …
 Using Annotations, default config
Microservices

21. | p. 21
 HTTP methods
 GET, PUT, POST, DELETE, PATCH, HEAD, ...
 Content Negotiation
 HTTP request header
 Automatically serve different formats using same URL
REST

22. | p. 22
 Initially developed by Yammer
 http://www.dropwizard.io
 Modular but “opinionated”
 Jetty server, Jersey JAX-RS, Jackson JSON, Metrics
 Very good for REST
 Less suitable for front-end apps
 Easy deployment
 1 “uberjar” (no need for Docker ?)
Dropwizard

23. | p. 23
Notes
 Small hack for file type / language negotiation
 For “human-friendly” HTML view
 Use Jetty UriConnegFilter
 Not intended for multiple vhosts, heavy
caching
 Proxy / web server in front
 Authentication
 Maybe Pac4j (3rd
party): http://www.pac4j.org/

24. Thanks !
Bart Hanssens / Fedict
WTC III, Simon Bolivarlaan 30
1000 Brussels, Belgium
@BartHanssens
bart.hanssens [at] fedict.be | www.fedict.belgium.be