Triple Stores

Triple Stores

Dr. Stephan Volmer

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sparkler! by Vicky Brock
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C

Dr. Stephan Volmer
© Zühlke 2013

Triple Stores
No talk without agenda!

• NoSQL

• Semantic

Web

• RDF
• SPARQL
• Triple

Stores

• Conclusions

Triple Stores | Dr. Stephan Volmer

Slide 2 of 34

© Zühlke 2013

NoSQL
New generation of modern web-scale databases!

big data

distributed
schema-free

relaxed consistency
open source

non-relational

simple interface
semi-structured data

eventually consistent
easy replication

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© Zühlke 2013

NoSQL
New generation of modern web-scale databases!

XML Databases

Multi-Model Databases
Object Databases

Column Family Stores
Graph Databases

Key Value Stores

Triple Stores

Document Databases

Wide Column Stores
Multi-Value Databases

Grid & Cloud Database Solutions
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© Zühlke 2013

Sematic Web
The tale of unstructured data and standardized metadata…

• Unstructured

common

• How

data is becoming more and more

do we best handle unstructured data?
Relational databases are not the answer!

• Metadata

helps describe the content of
unstructured data

• Creating

a standard will help push forward the
semantic web


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© Zühlke 2013

Semantic Web
The semantic web is a web of data!
•

Collaborative movement led by the international standards body,
the World Wide Web Consortium (W3C)

•

Promotes common data formats on the World Wide Web

•

Aims at converting the current web dominated by unstructured
and semi-structured documents into a “web of data”

“The Semantic Web provides a common framework that allows
data to be shared and reused across applications, enterprises,
and community boundaries.”

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© Zühlke 2013

Semantic Web
The semantic web stack!

User Interface & Applications
Trust

Proof
Logic
Querying

Rules

Taxonomies

Data Interchange

Cryptography

Ontologies

Syntax
Identifiers

Character Set
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© Zühlke 2013

Semantic Web
The semantic web stack!

Trust

Proof
Logic


Rules

Cryptography

Ontologies

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© Zühlke 2013

RDF
RDF? YAA, RTFM & STFU!

Resource Description Framework
•

Family of standards from W3C
http://www.w3c.org/RDF/

•

Make statements about things
The sky has the colour blue

•

Syntax is XML
<sky colour="blue"/>


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© Zühlke 2013

RDF
RDF makes statements about things!

The sky has the colour blue
thing

property

value

triple


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© Zühlke 2013

RDF
Syntax and semantics, baby!

• Triple
• Any

data structure is a simple EAV model

data structure can be represented as triples

E

A

V

<entity>

<attribute-value>

<value>

<subject>

<predicate>

<object>

• RDF

uses different terminology


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© Zühlke 2013

RDF
URIs in RDF – we no speak americano!

• Provide

namespaces to uniquely name the things
we want to talk about

• Provide

a way to identify the properties and types
of things in a way that is sharable and unique

• Anyone

can say anything about any things with a
shared URI identifier


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© Zühlke 2013

RDF
URIs in RDF – we no speak americano!

• Subject
• Object

and Predicate are always URIs

is either a literal or another URI

<http://www.zuehlke.com/person/stv>
subject
<http://www.zuehlke.com/properties/name>
predicate
"Stephan Volmer"
object
<http://www.zuehlke.com/properties/business-unit>
<http://www.zuehlke.com/business-units/dns>
<http://www.zuehlke.com/properties/email>
"stephan.volmer@zuehlke.com"

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© Zühlke 2013

RDF
RDF graphs are the glue to make it work.

• Logical
• One

collection of triples

store may contain many graphs

• Named

graphs are RDF graphs with a
URI name often called the context

• Graphs

–
–
–
–
–

can be targeted

import data into a graphs
export a graph
query / update data in graph
merging data from different sources
controlling access to data


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© Zühlke 2013

RDF
Namespaces, ontologies and other fun stuff…
prefix:person
prefix:company
prefix:ont
prefix:rdf

<http://www.zuehlke.com/person/>
<http://www.zuehlle.com/company/>
<http://www.zuehlke.com/types/>
<http://www.w3c.org/1999/02/22-rdf-syntax-ns#>

person:stv
person:stv
person:stv
person:stv

rdf:type
ont:worksfor
ont:name
ont:email

ont:person
company:zuehlke
"Stephan Volmer"
"stephan.volmer@zuehlke.com"

vocabulary

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© Zühlke 2013

RDF
Say good-bye to schema-free, say hello to schema-less!

• Core
• Any

RDF is schema-free

shape of data can be poured into a triple store

• Sometimes

a common ontology is helpful for
sharing and reusing knowledge bases

RDF
Schema

• Set

of RDF properties for defining
types and their constraints

• RDF

schema is expressed as RDF
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© Zühlke 2013

RDF
Behold, they are one people, and they have all one language!
prefix:person
prefix:ont
prefix:rdf
prefix:rdfs
prefix:xsd

<http://www.zuehlke.com/person/>
<http://www.zuehlke.com/types/>
<http://www.w3c.org/1999/02/22-rdf-syntax-ns#>
<http://www.w3.org/2000/01/rdf-schema#>
<http://www.w3.org/2001/XMLSchema#>

person:stv
ont:person
ont:age
ont:age
ont:age

rdf:type
rdf:type
rdf:type
rdfs:domain
rdfs:range


ont:person
rdfs:class
rdfs:property
ont:person
xsd:integer

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© Zühlke 2013

RDF
RDF object models – bridging the gap!
• Associations
• Maps

• Mapping

are at the heart of the RDF model

well to object models with entity to entity associations

between object model and RDF model can be loose

• Object

model can be a subset of the RDF model

• Allows

much greater flexibility in evolving the model and data
independently

• RDF

is schema-less, but schema languages are available

• It

is possible to impose constraints in the RDF model


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© Zühlke 2013

RDF
RDF object models – bridging the gap!
var stv = this.context.Persons.Create(‚stv‛);
person:stv rdf:type ont:person
var zuehlke = this.context.Companies.Create(‚zuehlke‛);
company:zuehlke rdf: type:company
stv.Age = 44;
person:stv ont:age ‘41’^^xsd:integer
stv.Labels.Add(‚Lead Software Archtitect‛);
stv.Labels.Add(‚Frankfurt‛);
person:stv ont:label ‚Lead Software Architect‛
person:stv ont:label ‚Frankfurt‛
stv.WorksFor = zuehlke;
person:stv ont:worksfor company:zuehlke

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© Zühlke 2013

RDF
Wait! Haven’t we seen something similar before?

{
"person":
{
"id": "stv",
"age": 44,
"labels": [ "Lead Software Architect", "Frankfurt" ],
"worksfor":
{
"company":
{
"id": "zuehlke"
}
}
}
}

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© Zühlke 2013

RDF
Chimera or visionary idea?

Google recently purchased the biggest RDF-based
encyclopedia on the Web and formally announced
that it will use RDF embedded in Web pages.
Yahoo and Microsoft are following
their footsteps.


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© Zühlke 2013

SPARQL
Ariadne's thread to the rescue!

Trust

Proof
Logic
Rules

Taxonomies
Data Interchange

Cryptography

Ontologies

Syntax
Identifiers

Character Set
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© Zühlke 2013

SPARQL
The Babel fish is probably the oddest thing in the universe.

• SPARQL

is pronounced “sparkle”

• SPARQL

is a recursive acronym for

SPARQL Protocol and RDF Query Language
• SPARQL

became an offical W3C recommendation

• SPARQL

allows for a query to consist of

in 2008
–
–
–
–

triple patterns,
conjunctions,
disjunctions, and
optional patterns


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© Zühlke 2013

SPARQL
A scientific dialect is perfected when its terms are hard to
understand.

• Returns

all triples in a store

select * where {
?a ?b ?c
}

• Returns

all persons

select ?a where {
?a rdf:type ont:person
}


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© Zühlke 2013

SPARQL
A scientific dialect is perfected when its terms are hard to
understand.

• Returns

all persons with an age sorted by age

select ?a ?age where {
?a rdf:type ont:person .
?a ont:age ?age
}
order by ?age

• Returns

all persons over 18

select ?a where {
?a rdf:type ont:person .
?a ont:age ?age .
filter (?age > 18)
}

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© Zühlke 2013

SPARQL
Finally, bringing matters to the head…

• Change

all Bills to Williams

FOAF Vocabulary Specification 0.98
prefix:foaf

<http://xmlns.com/foaf/0.1/>

delete {
?person foaf:givenName ‚Bill‛
}
insert {
?person foaf:givenName ‚William‛
}
where {
?person foaf:givenName ‚Bill‛
}


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© Zühlke 2013

SPARQL
Finally, bringing matters to the head…

• Delete

old book records

Dublin Core Metadata Initiative
prefix:dcmi
prefix:xsd

<http://purl.org/dc/elements/1.1/>
<http://www.w3.org/2001/XMLSchema#>

delete {
?book ?p ?v
}
where {
?book dcmi:date ?date .
filter ( ?date < ‚1970-01-01T00:00-02:00‛^^xsd:dateTime )
?book ?p ?v
}


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© Zühlke 2013

Triple Stores
A home for homeless triples.

• Persistent
• Core

data store for the RDF model

data structures are triples and graphs

• Triple

stores are usually transactional

• Several

standards for triple stores are proposed

– RDF
– RDFS
– SPARQL

• Standardization

is critical
Standardization is what made SQL popular


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© Zühlke 2013

Triple Stores
Triple stores fall into three broad categories!

In-Memory
Stores

Transient storage of triples in memory

Native
Stores

Persistent storage of triples in native stores
with their own storage implementation

Non-Native
Stores

Persistent storage of triples on top of thirdparty databases


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© Zühlke 2013

Triple Stores
Architecture should speak of its time and place…

Triple Store Operations

SPARQL Protocol

• Insert/delete
• Batch

REST Interface
API

insert/delete

• Export

graph

• SPARQL

update

Transaction
Processor

query

• SPARQL

SPARQL
Processor


Triple Store

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© Zühlke 2013

Conclusions
Do triple stores offer an advantage over relational
databases?

• No

need to create schemas

• No

need to link tables because you can have one
to many relationships directly

• Can

create new predicates (columns) on the fly

• Can

run queries off of this data just like in a
relational database model


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© Zühlke 2013

Conclusions
Will triple stores eventually replace relational databases?

• Triple

stores offer more flexibility

• More

powerful and complex queries can be written

• In

the short term relational databases and triple
stores will live side by side

• Many

companies are already using triple stores on
top of their existing relational databases


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© Zühlke 2013

Dr. Stephan Volmer

stephan.volmer@zuehlke.com

linkedin.com/in/svolmer/
 @stvzeg

Triple Stores

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