This document discusses semantic data and technologies for storing and querying semantic data. It provides an overview of semantic data and the semantic web. It then discusses specific technologies for storing semantic data such as triple stores and databases. It also covers query languages for semantic data like SPARQL and provides examples of semantic data storage systems and architectures.

1. Semantic
Semantic CMS Community Data
Access
Lecturer
Organization
Date of presentation
Co-funded by the
1 Copyright IKS Consortium
European Union

2. Page:
Part I: Foundations
(1) Introduction of Content Foundations of Semantic
(2)
Management Web Technologies
Part II: Semantic Content Part III: Methodologies
Management
Knowledge Interaction Requirements Engineering
(3) (7)
and Presentation for Semantic CMS
(4) Knowledge Representation
and Reasoning
(8)
Designing
Semantic CMS
Semantifying
(5) Semantic Lifting (9) your CMS
Storing and Accessing Designing Interactive
(6) Semantic Data
(10) Ubiquitous IS
www.iks-project.eu Copyright IKS Consortium

3. Page: 3
What is this Lecture about?
 We have learned ... Part II: Semantic Content
 ... which languages can be used Management
to model knowledge. Knowledge Interaction
(3)
 ... how to extract knowledge and Presentation
from content in a automatic way
(semantic lifting). (4) Knowledge Representation
and Reasoning
 We need a way ... (5) Semantic Lifting
 ... to store the extracted
Storing and Accessing
knowledge technically in an (6) Semantic Data
accessible way.
www.iks-project.eu Copyright IKS Consortium

4. Page: 4
Outline
 Semantic Data
 Semantic Web
 RDF
 Semantic Data Storage
 Triple Stores
 Semantic Data Access
 SPARQL
 RQL
 API Calls
www.iks-project.eu Copyright IKS Consortium

5. Page: 5
Semantic Data
 Stands for machine understandable information
 Allows computers to figure out the data without user
interference
 Allows computers act intelligently without programming
for each task
www.iks-project.eu Copyright IKS Consortium

6. Page: 6
Semantic Data
 Provides infrastructure to get practical results
 Applications find out subsequent information based on the
previous relations. (e.g. Eiffel Tower -> Paris -> France)
 Allows reasoning capabilities
 Providing extraction of related information which is not
directly linked
www.iks-project.eu Copyright IKS Consortium

7. Page: 7
Semantic Web
 A classical generic description:
 “Web of data”
 Extends the World Wide Web
 By encouraging,
 Common language for representing data
 Transformable to/from disparate sources such as relational
databases, XML, etc (RDF)
 Common reusable data model to represent data from different
domains in common terms (RDFS, OWL, etc)
 Rules to enable applications reason over the information
(SWRL)
www.iks-project.eu Copyright IKS Consortium

8. Page: 8
Semantic Web Layer Cake
Semantic Web Layer Cake, Image source: http://www.w3.org/2007/03/layerCake.svg
www.iks-project.eu Copyright IKS Consortium

9. Page: 9
Semantic Web
 So many organizations publishing their data in different
domains
 Media
 Geographic
 Government
 …
 Whole set contains approximately 30 billion triples
 One of the largest collections is DBPEDIA
 Semantified version of Wikipedia
 Example:
 Obtain cities of China that have population over 20 million
 Needs efficient storage and query for semantic data
www.iks-project.eu Copyright IKS Consortium

10. Page: 10
Representation of Semantic
Data
 RDF
 The common data format
 An abstract model with several serialization formats
 Consists of statement referred as triples having the form
(subject, predicate, object) where,
 Subject: any resource identifier
 Predicate: a resource identifier of any property
 Object: either a resource identifier or a literal value
www.iks-project.eu Copyright IKS Consortium

11. Page: 11
Storing Semantic Data
 Need for specialized designs for triple collections
 Two modalities:
 Relational databases
 Triple stores
 Mostly used for storage
 Lots of implementations
 They can also be RDB based.
www.iks-project.eu Copyright IKS Consortium

12. Page: 12
Triple Store
 A purpose-built database for the storage and retrieval of
RDF data.
 Optimized place to add, remove and query for triples.
Each triple in the TripleStore complies with the form
(subject, predicate, object)
www.iks-project.eu Copyright IKS Consortium

13. Page: 13
Considering XML Databases
 XML databases are existing storage systems for semi-
structured data
 Idea: Transform RDF to XML and store it in XML databases
 Yet, XML data model is not exactly same with semantic data
 XML data model is a tree-like structure
 RDF data is represented through a graph without an hierarchy
www.iks-project.eu Copyright IKS Consortium

14. Page: 14
Considering XML Databases
 XML Databases are not suitable for storage and querying
RDF
 Only simple manipulations can be handled through XML query
languages
 RDF Schema processing and inference is not possible
 Standard RDF/XML mapping is unsuitable
www.iks-project.eu Copyright IKS Consortium

15. Page: 15
Monolithic approach for DB
Based Triple Stores
 Generic representation for all RDF schemas
 Only two tables are used
 Resources table
 Triples table
www.iks-project.eu Copyright IKS Consortium

16. Page: 16
Monolithic approach for DB
Based Triple Stores
predid subid objid objvalue id uri
6 2 1 1 http://www.iks.og/topics.rdfs#Hotel
5 3 7 2 http://www.iks.og/topics.rdfs#HotelDirections
5 1 8 3 http://www.oclc.org/dublincore.rdfs#title
5 9 2 4 http://www.iks.og/schema.rdf#Ext.Resource
5 http://www.w3.org/1999/02/22-rdf-syntax-ns#type
3 9 Sunscal
e 6 http://www.w3.org/2000/01/rdf-schema#subClassOf
7 http://www.w3.org/1999/02/22-rdf-syntax-
ns#Property
8 http://www.w3.org/2000/01/rdf-schema#Class
9 rl
www.iks-project.eu Copyright IKS Consortium

17. Page: 17
Triples Stores
 Can be categorized into 3 category:
 In memory triple stores
 Used for certain operations like benchmarking, caching, etc
 Native triple stores
 Provides their own implementations (Virtuoso, Mulgara,
AllegroGraph, …)
 Non memory non native triple stores
 Are built on third party databases (Jena SDB, Kaon, …)
www.iks-project.eu Copyright IKS Consortium

18. Page: 18
Functionalities provided by
Triple Stores
 RDBMS-support
 General RDF model access
 Query language support in the store such as RQL,
SPARQL
 Some stores provide:
 Provenance - tracking of who-said-what
 APIs for accessing triple store over network
 Very few stores provide:
 Full text search
 Inference and rule languages
www.iks-project.eu Copyright IKS Consortium

19. Page: 19
Example Triple Store implementations
 RDF Suite
 Sofia Alexaki, Vassilis Christophides, Gregory Karvounarakis,
Dimitris Plexousakis, Karsten Tolle. The ICS-FORTH RDFSuite:
Managing Voluminous RDF Description Bases , SemWeb, 2001
 Based on an ORDBMS model
 Sesame
 http://www.openrdf.org/
 Relational databases (mysql, postgres, oracle)
 Jena
 http://www.hpl.hp.com/semweb/jena2.htm
 Relational databases (mysql , postgres, oracle)
 Virtuoso
 http://virtuoso.openlinksw.com/
 Native RDF Quad Storage (Physical Quads)
www.iks-project.eu Copyright IKS Consortium

20. Page: 20
RDFSuite (ICS-Forth)*
* IST-1999-13479 C-Web, IST-2000-26074 Mesmuses
www.iks-project.eu Copyright IKS Consortium

21. Page: 21
How triples are stored and
accessed in RDF Suite
 Separate tables are created to store resources
 Properties, subClasses, subProperties and instances
 Indiceson attributes like URI, source and target
 Querying is possible through RQL
www.iks-project.eu Copyright IKS Consortium

22. Page: 22
How triples are stored and
accessed in RDF Suite
[Figure from *]
www.iks-project.eu Copyright IKS Consortium
*Sofia Alexaki, Vassilis Christophides, Gregory Karvounarakis, Dimitris Plexousakis, Karsten Tolle. The ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases , SemWeb, 2001

23. Page: 23
Sesame Architecture
 DBMS-independent API for
accessing triple
repositories
 SAIL API
 A set of Java interfaces
between other modules and
repository
 Abstract from the actual
storage mechanism
 Query Module
 RQL support
 Different ways to
communicate with clients
 Through Protocol handlers
www.iks-project.eu Copyright IKS Consortium
*Jeen Broekstra and Arjohn Kampman and Frank van Harmelen, Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema, Proceedings of the First International
Semantic Web Conference, 2002

24. Page: 24
SAIL API over PostgreSQL
 PostgreSQL
 Object-relational
DBMS
 Support sub-table
relations between its
tables for providing
RDF Schema class
and property
subsumption
 Individuals are
represented under
separate tables
created for resources
 Difficult to add table
www.iks-project.eu Copyright IKS Consortium
*Jeen Broekstra and Arjohn Kampman and Frank van Harmelen, Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema, Proceedings of the First International
Semantic Web Conference, 2002

25. Page: 25
SAIL API over MySQL
 MySQL
 The database
schema does
not change
when the
RDFS changes
 Has advantage
where RDFS is
unstable
www.iks-project.eu Copyright IKS Consortium
*Jeen Broekstra and Arjohn Kampman and Frank van Harmelen, Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema, Proceedings of the First International
Semantic Web Conference, 2002

26. Page: 26
Jena2 Architecture
www.iks-project.eu Copyright IKS Consortium

27. Page: 27
Jena2 Architecture
www.iks-project.eu Copyright IKS Consortium
*Kevin Wilkinson, Craig Sayers, Harumi A. Kuno, Dave Reynolds: Efficient RDF Storage and Retrieval in Jena2, Proceedings of SWDB'03, The first International Workshop on
Semantic Web and Databases

28. Page: 28
Jena2
 Jena2
 Denormalized schema
 Avoids unnecessary joins by merging URIs, literals in
statements table
 Multiple statement tables
 Better locality and caching
 Property Tables
www.iks-project.eu Copyright IKS Consortium

29. Page: 29
Normalized vs Denormalized
Tables
www.iks-project.eu Copyright IKS Consortium

30. Page: 30
Property Tables
Triple Store Only Person Property Table
Subject Property Object
ID name age gender
person1 name Alice
p1 Alice 32 -
person1 age 32
p2 Bob 35 male
person1 twinOf person2
person1 faxPhone x1234 Triple Store
person1 adminPh x5678 Subject Property Object
person2 name Bob person1 twinOf person2
person2 age 35 person1 faxPhone x1234
person2 adopteeOf person6 person1 adminPh x5678
person2 friendOf person8 person2 adopteeOf person6
person2 gender male person2 friendOf person8
www.iks-project.eu Copyright IKS Consortium
*Kevin Wilkinson, Craig Sayers, Harumi A. Kuno, Dave Reynolds: Efficient RDF Storage and Retrieval in Jena2, Proceedings of SWDB'03, The first International Workshop on
Semantic Web and Databases

31. Page: 31
Jena Persistence Options
 SDB
 Scalable storage and query for RDF
 Specifically designed for SPARQL support
 Supports: MySQL, PostgreSQL, Oracle 11g, Microsoft
SQL server and IBM DB2
 Scales to graphs of 100 million triples
www.iks-project.eu Copyright IKS Consortium

32. Page: 32
Jena Persistence Options
 TDB
 Provides for large scale storage and query of RDF
datasets using a pure Java engine
 Supports SPARQL
 A non-transactional, faster database solution for use by a
single system
 It scales well beyond SDB and is simpler to setup
www.iks-project.eu Copyright IKS Consortium

33. Page: 33
Virtuoso
 General purpose RDBMS with extensive RDF
adaptations
 RDF data is stored as RDF quads, i.e. it supports RDF
with named graphs
 i.e. graph, subject, predicate, object tuples
 The columns are G for graph, P for predicate, S for subject
and O for object
www.iks-project.eu Copyright IKS Consortium

34. Page: 34
Querying Semantic Data
 Semantic data can be queried from triple stores by
 Various query languages
 SPARQL
 Different endpoints provided
 RQL
 RDQL
 SeRQL
…
 API Calls
 Through proprietary APIs of different projects
 Linked Data
www.iks-project.eu Copyright IKS Consortium

35. Page: 35
SPARQL
 Is an RDF query language
 Standardized by W3C consortium
 Similar concept of SQL for databases
 Syntactically
resembles to SQL
 RDF Graphs instead of databases
www.iks-project.eu Copyright IKS Consortium

36. Page: 36
SPARQL Endpoints
 Provides functionality to query the knowledge base via
the SPARQL language
 Accepts queries and returns results through HTTP
protocol
 Query results can be in different formats such as
 RDF
 XML
 HTML
 JSON
 CSV
www.iks-project.eu Copyright IKS Consortium

37. Page: 37
Semantic Data Access With API
Calls
 Open source projects provides APIs to manipulate RDF
data
 Jena
 Apache Clerezza
 Sesame
 JRDF
www.iks-project.eu Copyright IKS Consortium

38. Page: 38
Jena
 Jenaprovides a rich API to manipulate the RDF stored in
the underlying triple store.
 Model to represent graphs
 CRUD methods for triples
 Querying methods for existing resources
 See the next slide for the code snippet…
www.iks-project.eu Copyright IKS Consortium

39. Page: 39
Jena Code Snippet
String personURI = "http://somewhere/JohnSmith";
String givenName = "John";
String familyName = "Smith";
String fullName = givenName + " " + familyName;
// create an empty Model which represents an RDF graph
Model model = ModelFactory.createDefaultModel();
// create the resource which will produce the triples in the next slide
Resource johnSmith
= model.createResource(personURI)
.addProperty(VCARD.FN, fullName)
.addProperty(VCARD.N,
model.createResource()
.addProperty(VCARD.Given, givenName)
.addProperty(VCARD.Family, familyName));
www.iks-project.eu Copyright IKS Consortium

40. Page: 40
Jena
 Created triples with the code snippet in previous slide:
(<http://somewhere/JohnSmith>, VCARD.FN, “John
Smith”)
(<http://somewhere/JohnSmith>, VCARD.FN, _)
(_, VCARD.Given, “John”)
(_, VCARD.Family, “Smith”)
• Note that _ symbol represents a blank node
www.iks-project.eu Copyright IKS Consortium

41. Page: 41
Apache Clerezza
 Provides an API regardless from the different triples
stores it supports
 Its API provides a model to represent RDF graphs and
manipulate those graphs
 Also provides an SPARQL endpoint to query the stored
knowledge
www.iks-project.eu Copyright IKS Consortium

42. Page: 42
Apache Clerezza Code Snippet
 Simple code snippet adding two triples to the graph:
String base = “http://www.example.org#”;
MGraph g = new SimpleMGraph();
g.add( new TripleImpl(
new UriRef(base + “JohnSmith”),
new UriRef(rdf:Type)
new UriRef(foaf:Person)));
g.add( new TripleImpl(
new UriRef(base + “JohnSmith”),
new UriRef(VCARD:FN)
LiteralFactory.getInstance().createTypedLiteral(“John”)));
www.iks-project.eu Copyright IKS Consortium

43. Page: 43
Linked Data
 Interrelated datasets on the Web so that computers can
explore them
 Has a standard format to be accessed and managed
 Provides integration and reasoning on a huge amount
of data on the Web
www.iks-project.eu Copyright IKS Consortium

44. Page: 44
Linked Data
 Fourfamous principles of linked data represented by
Tim Berners-Lee
 Use URIs as names of things
 Use HTTP URIs to provide dereferencable data to people
 When an URI is dereferenced provide useful information in
standard format (RDF, SPARQL)
 Provide links to other URIs to make possible discovery of
related data
www.iks-project.eu Copyright IKS Consortium

45. Page: 45
Linked Data
www.iks-project.eu Copyright IKS Consortium

46. Page: 46
Linking Open Data Project
 Isan W3C SWEO Project
 Aims to make data freely to everyone
 Aims to publish open data sets as RDF and set
semantic relationships between them
 Serves information in a machine readable format
 Enriches content
 Reduces duplication
 Linked datasets increasing rapidly
 A large number of datasets are linked already
www.iks-project.eu Copyright IKS Consortium

47. Page: 47
Linked Datasets As of October
2008
www.iks-project.eu Copyright IKS Consortium

48. Page: 48
Linked Datasets As of September
2010
www.iks-project.eu Copyright IKS Consortium

49. Page: 49
2011
www.iks-project.eu Copyright IKS Consortium

50. Page: 50
Access Data In The Cloud
 Follow the RDF links representing the “things”
 SPARQL Endpoints
 Ready to use software to discover linked data (See the
next slide)
www.iks-project.eu Copyright IKS Consortium

51. Page: 51
Linked Data Applications
 Lots of application on top of the linked data
 Tabulator
 Marbles
 Openlink RDF Browser
 …
 Just google
 RDF Crawlers
 RDF Browsers
 Also see the following link containing a number of linked data
applications:
 http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/
LinkingOpenData/Applications
www.iks-project.eu Copyright IKS Consortium

52. Page: 52
Available SPARQL Endpoints
 http://dbpedia.org/sparql
 http://www4.wiwiss.fu-berlin.de/dblp/
 Tosee possible SPARQL endpoints providing a certain
URI see
 http://void.rkbexplorer.com/endpoint-search/
 See also a list of alive SPARQL endpoints
 http://www.w3.org/wiki/SparqlEndpoints
www.iks-project.eu Copyright IKS Consortium

53. Page: 53
References
 http://www.w3.org/TR/rdf-sparql-query
 http://jena.sourceforge.net/tutorial/RDF_API/index.html
 http://www.slideshare.net/ldodds/sparql-tutorial
 http://www.slideshare.net/shamod/a-hands-on-overview-of-the-semantic-
web?src=related_normal&rel=1702851
 http://www.cambridgesemantics.com/2008/09/sparql-by-example
 http://linkeddata-specs.info/
 http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
 http://www.bioontology.org/wiki/images/6/6a/Triple_Stores.pdf
 Sofia Alexaki, Vassilis Christophides, Gregory Karvounarakis, Dimitris Plexousakis, Karsten Tolle. The
ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases , SemWeb, 2001
 Jeen Broekstra and Arjohn Kampman and Frank van Harmelen, Sesame: A Generic Architecture for
Storing and Querying RDF and RDF Schema, Proceedings of the First International, Semantic Web
Conference, 2002
 Kevin Wilkinson, Craig Sayers, Harumi A. Kuno, Dave Reynolds: Efficient RDF Storage and Retrieval in
Jena2, Proceedings of SWDB'03, The first International Workshop on Semantic Web and Databases
 http://jena.sourceforge.net/DB/index.html
 http://virtuoso.openlinksw.com/
www.iks-project.eu Copyright IKS Consortium