Semantic Technologies for CMS: Query SPARQL for Jaguar Models and Prices

Semantic
Technologies
for CMS
Semantic CMS Community

Dr. Tilman Becker
DFKI GmbH,
Saarbrücken, Germany

OpenCMS Days,
Cologne
September 25, 2012

Co-funded by the
1 Copyright Tilman Becker, DFKI
European Union

Page: 2

Web evolution

3.0
Web
Web
4.0

Web
1
.0
Web
2.0

Slide by Nova Spivack, Radar Networks
www.iks-project.eu Copyright IKS Consortium

Page: 3

What is the problem?

www.iks-project.eu Tilman Becker, DFKI

Page: 4

The Semantic Web
  Thevision of the Semantic Web has been originally
proposed by Tim Berners-Lee

  “TheSemantic Web is not a separate Web but an
extension of the current one, in which information is
given well-defined meaning, better enabling computers
and people to work in cooperation.” [The Semantic
Web, 2001]

  Standardized specification techniques for the semantic
annotation of content (RDF, OWL, ...)

Page: 5

Semantic Web Stack
  W3C provides standardized
specifications for Semantic
Web technologies
  Semantic Web Layer Cake
as a conceptual architecture
describes an hierarchy of
languages
  Each layer exploits and uses
capabilities of the layers
below
Semantic Web Layer Cake, Image source: http://www.w3.org/2007/03/layerCake.svg


Page: 6

A Few Semantic Web Concepts
  Identification:
URI
  Statements: RDF
  Queries: SPARQL
  Storage: Triple Stores
  Ontologies: OWL
  Is there anybody out there: Linked Open Data

  Semantic Lifting


Page: 7

Unique Identification of
Resources
  “...more fundamental than either HTTP or HTML are
URIs, which are simple text strings that refer to Internet
resources -- documents, resources, people, and
indirectly to anything. URIs are the glue that binds the
Web together.”

  In
a “Web of Data” the unique
identification of entities is required


Page: 8

How to identify resources?
  URI – Uniform Resource Identifier [RFC 3986]
  “A Uniform Resource Identifier (URI) is a compact
sequence of characters that identifies an abstract or
physical resource.”
  A URI consists of five parts: scheme, authority, path, query
and fragment
  URI = scheme ":" authority "/" path [ "?" query ] [ "#"
fragment ]
  Example:

http://user@example.com:8042/over/there?name=ferret#nose
scheme authority path query fragment

Page: 9

What do we need?
  We want to express the statement:
  “The brand of the car is Jaguar.”

  We need ...
  ...a way to address the concrete resource car.
  ... to express the property brand of the resource car.
  ... to define the property value Jaguar for the property
brand.


Page: 10

Resource Description
Framework (RDF)
  “TheResource Description Framework (RDF) identifies
things using Web identifiers (URIs), and describes
resources with properties and property values.”
  A Resource is an object that can be identified by an
URI, e.g. “http://example.org/Car”.
  A Property describes an aspect of a resource, e.g.
“http://example.org/Brand”. The property is also
identified by an URI.
  TheProperty value assigns a concrete value to a
property, e.g. “Jaguar” or ““http://example.org/Jaguar”.
www.iks-project.eu http://www.w3schools.com/rdf/ Copyright IKS Consortium

Page: 11

RDF Statements
  RDF statements consist of subject (resource), predicate
(property) and object (property value)
Predicate Object
Subject
(URI)

Predicate

Object
(literal)

  Subjects (except Blank Nodes) and Predicates are
always defined by URIs
  Objects can be defined by URIs and literals

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RDF Statements - Example
  Exemplary statements:
  “The brand of the car is Jaguar.”
  “The model of the car is XF.”
Subject Predicate Object

http://example.org/rel/Brand
http://example.org/Car http://example.org/Jaguar

http://example.org/rel/Model

XF Predicate

Object

Page: 13

Resource Description
Framework (RDF)
  “The Resource Description Framework (RDF) is a
language for representing information about
resources...” [RDF Primer]

•  W3C Standard (http://www.w3.org/RDF)

  RDF provides a graph-based data model
  for representing metadata
  for describing the semantics of
information in a machine-accessible way

Page: 14

RDF Serialization Formats
  RDF/XML
  N3
  N-Triples
  TRiG
  TRiX
  Turtle
  JSON
  JSON-LD
  RDFa


Page: 15

Semantic Layer Web Cake

A model for describing
resources with properties A format for specifying structured
and property values. data in a machine-readable form

Unique identification
of resources

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RDF Queries
  RDFprovides a model for describing resources with
properties and property values.

@prefix
ex:
<http://www.example.org/>.

ex:Car1

ex:Brand

ex:Jaguar

ex:Car1

ex:Colour
“Black”

t all
ex:Car2

ex:Brand

ex:Jaguar

I ge ?
ex:Car2

ex:Colour
“White”

do
ex:Car3

ex:Brand

ex:VW

How Jaguars
ex:Car3

ex:Colour
“Black”

b lack

Page: 17

SPARQL
  SPARQL Protocol and RDF Query Language
  W3C Recommendation since 2008

  SPARQL provides a standard for querying information,
that is specified in RDF

  SPARQL consists of three specifications
  Query language
  Query results XML format
  Data access protocol


Page: 18

Exemplary SPARQL Query
“Return the models and prices for all cars of brand
‘Jaguar’ ” Declares namespaces for
abbreviated resources
SPARQL Query: identifiers.
PREFIX
ex:

<http://example.org/>

Identifies the variables to
SELECT
?model
?price

WHERE

appear in the query

{
?car
ex:Brand

ex:Jaguar
.
results.

?car
ex:Model
?model
.

?car
ex:Price
?price
.

}

Provides the basic graph
pattern to match against
Exemplary Result: the data graph.
Model Price
“XJ” “79.750,00”
“XF” “44.900,00”

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Triple 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, …)


Page: 20

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


Page: 21

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)


Page: 22

Computational ontologies
  Ontologies are (software) components, expressed and
managed in standard W3C languages like RDF, OWL,
RIF, SPARQL
  Computational Ontologies are artifacts
  Have a structure (linguistic, logical, etc.)
  Their function is to “encode” a description of the
world (actual, possible, counterfactual, impossible,
desired, etc.) for some purpose

www.iks-project.eu

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Searching for ontologies on
the Semantic Web

www.iks-project.eu

Page: 24

From the lessons learnt ...
  Smallontologies with explicit documentation of design
rationales
  components supported by specific functionalities
  selection, matching, composition, etc.
  implemented in repositories, registries, catalogues,
open discussion and evaluation forums, and in new-
generation ontology design tools
  ontologydesignpattern.org
  ODP and Watson APIs
  NeOn ODP Plugin
  etc.

www.iks-project.eu

Page: 25

Ontology Design Patterns

An ontology design
pattern is a reusable
successful solution to a
recurrent modeling
problem

www.iks-project.eu

Page:

Align CMS Representation With
External Ontology
-NewsSubjectCodes -MeSH

-ArtsCultureEntertainment -Anatomy

-DisasterAccident -Diseases

-EconomyBusinessFinance -Organisms
-Education -Psychiatry
-EnvironmentalIssues -BehaviorMechanisms
-Health
Mesh
Representation of -BehaviorDisciplines
-Disease Biomedic
New Subject Codes as
-MentalDisorders al
hierarchical ontology
-HealthTreatment
classes -AnxietyDisorders Ontology
-Illness -EatingDisorders
-EatingDisorder
-SleepingDisorders
equivalentTo
-Obesity
-SomotoformDisorders
-Medicine

-SocialIssues
www.iks-project.eu

Page: 27

Why is RDFS not enough?
  RDFS cannot express negations

  Defined property restrictions are global

  Missing cardinalities for properties

  Relations between (sub-)classes (e.g. disjunction)


Page: 28

OWL – Web Ontology
Language
  “TheOWL Web Ontology Language is designed for use
by applications that need to process the content of
information instead of just presenting information to
humans.”
  OWL has been developed as a vocabulary extension of
RDF
  Explicitly
represents the meaning of terms in
vocabularies and the relationships between those
terms. (Ontology)

http://www.w3.org/TR/2004/REC-owl-features-20040210/

Page: 29

OWL – The Story
  2004 - OWL W3C Recommendation
  2009 - OWL 2 W3C Recommendation

OWL = Web Ontology Language
  Why not WOL?
  Obvious pronunciation which is easy on the ear http://piqs.de
  Opens up great opportunities for logos
  Owls are associated with wisdom
  It has an interesting back story

http://lists.w3.org/Archives/Public/www-webont-wg/2001Dec/0169.html

Page: 30

schema.org
  “simple”
ontology
  Designed for web search
  Contains movies and records, but not plants and animals
  Supported by
  Google
  Bing
  Yahoo!


Page: 31

Back to the Cake ...

Highly expressive ontology
language for modelling complex
A language for querying knowledge domains.
information specified in
RDF. A language for describing a
lightweight ontology.
A model for describing
resources with properties A format for specifying structured
and property values. data in a machine-readable form

Unique identification
of resources

Page: 32

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


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Linked Datasets As of October
2008


Page: 34

Linked Datasets As of September
2010


Page: 35

2011

Page: 36

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)


Page: 37

Linked Data Applications
  Lots of application on top of the linked data
  Tabulator
  Marbles
  Openlink RDF Browser
  …
  Just google
  RDF Crawlers
  RDF Browsers
  Alsosee the following link containing a number of linked data
applications:
  http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/
LinkingOpenData/Applications


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What is “Semantic Lifting”?
  Semantic Lifting refers to the process of associating
content items with suitable semantic objects as
metadata to turn “unstructured” content items into
semantic knowledge resources
  Semantic Lifting makes explicit “hidden” metadata in
content items

www.iks-project.eu Copyright IKS Consortium 38

Page:

Metadata: Variants
  Metadata exist in many forms:
  Free text descriptions
  Descriptive content related keywords or tags from fixed vocabularies or
in free form
  Taxonomic and classificatory labels
  Media specific metadata, such a mime-types, encoding, language, bit
rate
  Media-type specific structured metadata schemes such as EXIF for
photos, IPTC tags for images, ID3-tags for MP3, MPEG-7 for videos,
etc.
  Content related structured knowledge markup, e.g. to specify what
objects are shown in an image or mentioned in a text, what the actors
are doing, etc.


Page:

Publishing Web Content with
semantic metadata
  Augmenting web content with structured information becomes
increasingly important
  Several methods have emerged in recent years to include
structured metadata in Web pages
  Microformats
  RDFa
  Microdata (HTML5)
  Supported by the major search engines to improve search and
result presentation, e.g. Google („Rich Snippets), Bing, Yahoo


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Augmenting Web Content
  The HTML code contains a review of a restaurant in plain text
using only line breaks for structuring
  Without specialized information extraction analysis tools it cannot
be interpreted, e.g. that it is a review (of what and when?), who the
reviewer was, etc.

<div>
L’Amourita Pizza
Reviewed by Ulysses Grant on Jan 6.
Delicious, tasty pizza on Eastlake!
L'Amourita serves up traditional wood-fired Neapolitan-style pizza,
brought to your table promptly and without fuss. An ideal neighborhood
pizza joint.
Rating: 4.5
</div>


Page:

Microformats
  Same text but additional span elements with class attributes to
encode the type of contained information (hReview) and the
properties of that type
<div class="hreview">
<span class="item">
<span class="fn">L’Amourita Pizza</span>
</span>
Reviewed by <span class="reviewer">Ulysses Grant</span> on
<span class="dtreviewed">
Jan 6<span class="value-title" title="2009-01-06"></span>
</span>.
<span class="summary">Delicious, tasty pizza on Eastlake!</span>
<span class="description">L'Amourita serves up traditional wood-fired
Neapolitan-style pizza, brought to your table promptly and without fuss.
An ideal neighborhood pizza joint.</span>
Rating:
<span class="rating">4.5</span>
</div>


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RDFa
  Same text but additional attributes and span elements encoding a
RDF structure:
  namespace declaration of the used ontology
  RDF class encoded by typeof attribute and its properties by a
property attribute
<div xmlns:v="http://rdf.data-vocabulary.org/#" typeof="v:Review">
<span property="v:itemreviewed">L’Amourita Pizza</span>
Reviewed by
<span property="v:reviewer">Ulysses Grant</span> on
<span property="v:dtreviewed" content="2009-01-06">Jan 6</span>.
<span property="v:summary">Delicious, tasty pizza on Eastlake!</span>
<span property="v:description">L'Amourita serves up traditional wood-fired
Neapolitan-style pizza, brought to your table promptly and without fuss.
An ideal neighborhood pizza joint.</span>
Rating:
<span property="v:rating">4.5</span>
</div>


Page:

Microdata (HTML5)
  Same text but additional attributes and span elements:
  A class declaration as value of an itemtype attribute and its
properties as values of an itemprop attribute

<div>
<div itemscope itemtype="http://data-vocabulary.org/Review">
<span itemprop="itemreviewed">L’Amourita Pizza</span>
Reviewed by <span itemprop="reviewer">Ulysses Grant</span> on
<time itemprop="dtreviewed" datetime="2009-01-06">Jan 6</time>.
<span itemprop="summary">Delicious, tasty pizza in Eastlake!</span>
<span itemprop="description">L'Amourita serves up traditional wood-fired
Neapolitan-style pizza,
brought to your table promptly and without fuss. An ideal neighborhood pizza
joint.</span>
Rating: <span itemprop="rating">4.5</span>
</div>
</div>


Page:

Named Entities
  Statistical Approaches: examples
  Lingpipe: Hidden Markov Models
  OpenNLP: Maximum Entropy Models
  Stanford NER: Conditional Random Fields

  Statistical models crated by supervised learning techniques
  Large annotated corpora required
  Customization diffcult except by re-annotation/re-training
  Not suitable for any type of named entity


Page:

NER Markup for a Web Page


Page:

IE Template
A Person Template (as
Typed Featured Structure)
instantiated from text.
The template supports the
extraction of various
properties of a person.


Page:

Clustering
  Detection of classes in a data set
  Partitioning data into classes in an unsupervised way
with
high intra-class similarity
low inter-class similarity
  Main variants:
  Hierarchical clustering
  Agglomerative

  Partitioning clustering
  K-Means


Page:

NER Evaluation
  Nobel Prize Corpus from NYT, BBC, CNN
  538 documents (Ø 735 words/document)
  28948 person, 16948 organization occurrences

Sprout Calais Stanford OpenNLP
NER
Precision 77,26 94,22 73,21 57,69
Recall 65,85 86,66 73,62 42,86
F1 71,10 90,28 73,41 49,18


Page: 50

A Few Semantic Web Concepts
  Identification:
URI
  Statements: RDF
  Queries: SPARQL
  Storage: Triple Stores
  Ontologies: OWL
  Is there anybody out there: Linked Open Data

  Semantic Lifting


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Bringing it all together
  Exporting data (more datasets)
  Grab information from your content (i.e.,
recognize the „entities“)

  Merging your data
  Merge it from different data

  Conbine with different datasets/content
  Use data to interact with (e.g., configure)
web services

  Publishing Semantics/Content/interaction
  Enrich your content with dinamically
generated, interactive information


Page:



web services



Page: 2

Page: 55

IKS Goal

A Reference Architecture
for Semantically Enabled
Content Management Systems

Copyright IKS Consortium


Page: 56

Whatis a a Semantic CMS?
is Semantic CMS?
Page: 4

What

Traditional CMS vs. Semantic CMS

 Atomic unit: Document  Atomicunit: Entity
 Properties as meta-data  Semantic meta-data
 e.g. author  Defined entity types
 tags, keywords  Linked entities
 Keyword search for  Semantic search for
 strings in docs  entities and their relations
 Document Management  Knowledge Management
 Document types  Entity management
 Document workflow  Ontologies


Page: 57

Building Semantic CMS
  Ask the experts:
  Top 8 CMS Customer Needs
  Thefollowing list features the top 8 CMS capabilities that
are perceived as highly relevant by CMS customers. The
ranking is based on in-depth interviews with 12 IT
executives of CMS customer organizations in Europe.


Page: 58

Top 8 Customer Needs
  Interoperability
  Support for Content Creation
  Workflow management
  Multi-Channel Access to Content
  Personalization
  Enrichment of Content
  Intuitive User Interface
  Enhanced Search Functionality


Page: 59

Ask the experts

Book title: Semantic Technologies in Content Management
Systems - Applications, Trends and Evaluations
Editors: Wolfgang Maass, Saarland University, Germany; Tobias
Kowatsch, University of St. Gallen, Switzerland
Publisher: Springer, Heidelberg, Germany
ISBN: 978-3642215490 (1st Edition. 213 p. 56 illus. Hard cover)
Year: January 31, 2012


Page: 60

IKS guidelines

  Do not change existing CMS!

  Provide as much abstraction as possible!


Page: 61

Traditional CMS Architecture


Page: 62

Semantic CMS Architecture


Page: 63

Implementation of the
Reference Architecture
  Referenceimplementation within
the IKS project
  IKS: An open source community to
bring semantic technologies to CMS
platforms
  New incubating project at the
Apache Software Foundation
http://incubator.apache.org/stanbol


Page: 64

Do Not Replace – but Extend
Not Replace – but Extend
Page: 5

Do

 No need to replace your existing technology.
 IKS components offer service oriented integration.

Extend by Using Semantic Services

Traditional
CMS IKS
Technology
Stack
Database



Page: 65

Use on the Concepts of the Web
the Concepts of the Web
Page: 6

Rely

 Integration
through a RESTful web service API
 Resources are identified by their URI

HTTP
Request
Traditional
CMS IKS
HTTP Technology
Response Stack
Database



IKS Page: 66
9
Reference Implementation

IKS Semantic
User
IKS VIE

7.0
Widgets
Interface

Content Knowledge
Semantic User Interaction

IKS VIE

Knowledge Access

Knowledge Extraction Pipelines

Knowledge Administration
Stanbol
Apache Enhancement
Stanbol Engine
Enhancer

Apache Stanbol RESTful API
Reasoning
Apache
Stanbol
Reasoners Apache
Stanbol Rules

Console
OSGI
Knowledge Models
Apache
Apache
Stanbol
Clerezza
Ontology Manager

Content Apache
Stanbol Knowledge Repository
Repository CMS Adapter RDF
CMIS / Apache
JCR Stanbol
ContentHub Apache
Stanbol
EntityHub Apache
Stanbol
FactStore

Page: 67

Page: 10

VIE Quick Facts
VIE Quick Facts

 VIE is a utility library for semantic maintenance in
JavaScript
 Offers semantic web developers a DSL to ease
recurring tasks
 Easy access to embedded semantic annotations in HTML
(RDFa)
 Easy loading of properties for entities from external
services
 Easy saving of knowledge about entities
 Easy querying of semantic services

 VIE Widgets are web user interface components based
on VIE.
Copyright IKS Consortium
www.iks-project.eu

Page: 68
11

Apache Stanbol Quick Facts

 Modular (OSGi) components implemented in Java

Semantic Lifting
 Enhance content
 Link to Linked Open Data (LOD) sources
 Store and index enhanced content for search

Knowledge Representation & Reasoning
 Manage ontologies
 Apply rules to ontologies
 Reasoning over managed ontologies


Page: 69
12

Service-Oriented View
VIE - User Interface Layer
VIE
VIE
Widgets

Apache Stanbol Service Layer

Apache Apache Apache Apache
Stanbol Stanbol Stanbol Stanbol
Enhancer EntityHub Ontology Manager Reasoners

Apache Apache
Apache
Stanbol Stanbol
Stanbol Rules
ContentHub FactStore
Stanbol
Enhancement
Engines Apache
Stanbol Apache Stanbol
CMS Adapter Component Layer
Semantic Lifting
www.iks-project.eu KnowledgeCopyright IKS Consortium
Representation & Reasoning

Page: 70
14

Enhancer & Engines
Features
 Semantic lifting by automatically extracting entities from
textual content
 Different enhancement engines for specific tasks
 Engines are arranged in customizable enhancement
chains where one engine may rely on the output of
another engine
 Examples
 Language Identification Engine
 Named Entity Extraction Engine
 Geonames Engine to annotate places with additional
information from geonames.org


Page: 71
16

Entityhub
Features

 Manage a network of remote sites for fast entity lookup
 Caching of externally retrieved entity information
 CRUD management of local entities

 Examples
 Use DBPedia linked open data source to retrieve
additional information for entities
 Use a customized vocabulary for local entities


Page: 72
18

Contenthub
Features

 Document repository by indexing retrieved documents
 Supports indexing of additional semantic metadata
provided along the content
 Search facilities
 Keyword Search
 Faceted Search based on available semantic metadata


Page: 73
20

CMS Adapter
Features

 Bootstrapping component to import content from a CMS
into Apache Stanbol
 Import content from a CMIS/JCR compliant CMS into
the Apache Stanbol Contenthub


Page: 74
29

VIE & VIE Widgets
VIE - User Interface Layer
VIE
VIE
Widgets

Apache Stanbol Service Layer

Apache Apache Apache Apache
Stanbol Stanbol Stanbol Stanbol
Enhancer EntityHub Ontology Manager Reasoners

Apache Apache
Apache
Stanbol Stanbol
Stanbol Rules
ContentHub FactStore
Stanbol
Enhancement
Engines Apache
Stanbol Apache Stanbol
CMS Adapter Component Layer
Semantic Lifting
www.iks-project.eu KnowledgeCopyright IKS Consortium
Representation & Reasoning

Page: 75
30

VIE & VIE Widgets
Features

 VIE is a JavaScript library for implementing decoupled
CMS and semantic interaction in web applications
 VIE provides easy access to the semantic metadata
(RDFa) within a web page
 VIE Widgets are user interface components that
implement semantic user interactions
 Examples
 Semantic image search
 Automatic tagging of entities
 Semi-automatic content annotation


Page:

VIE: Core

Javascript
is a
framework/library

www.iks-project.eu

Page:

abstraction

VIE: Core
of
semantic entities
and their relations

Javascript
is a
framework/library

www.iks-project.eu

Page:

abstraction

VIE: Core
of
semantic entities
and their relations

using

Javascript
is a
framework/library

www.iks-project.eu

Page:

abstraction

VIE: Core
of
semantic entities
and their relations

using

Javascript
is a
framework/library

addr
essin
g

Web Developers
  bringing semantics into webpage
  without caring too much about
triples/triplestores and so on
www.iks-project.eu

Page:

VIE: Core
  VIE offers an API to: -
  create entities with properties
  link entities
  serialize entities (either into the HTML using RDFa or to a
server)
  access semantic lifting services (e.g., Zemanta,
OpenCalais, Apache Stanbol, …)
  query databases to fill
  The default "ontology" that VIE is delivered with, is
http://schema.org, which can be easily switched or
extended.

www.iks-project.eu

Page:

VIE: UI Widgets
On top of VIE we gathered a bunch
of UI widgets in a library that help to
simplifying embedding VIEs power
into a webpage more directly.

UI Widgets


Page:

VIE Widgets

Widgets Widgets

  VIE-Widgets are a sort of jQuery UI Widgets
in order to:
  achive maximum portability
  accelerating lerning curve


Page:

It‘s about abstraction

VIE - UI Widgets

„VIE-W“

VIE

VIE-2

„Edit your content w. Semantics“

„Edit your Semantics“

(Semantic) Services

(e.g., Stanbol Enhancer, - EntityHub, Zemanta, ...)

(Semantic) Databases

(e.g., DBPedia, Geonames, ...)


Page:

Analyze with Apache Stanbol
var elem = $('<p>This is a small test, where Steve Jobs
sings a song.</p>');
v
.analyze({element: elem})
.using('stanbol')
.execute()
.done(function(entities) { alert ("found: " + entities.length +
" entities!"})
.fail(function(f) { alert("something went wrong") });

www.iks-project.eu

Page:

Interaction Patterns: IP
An IP consists of four parts:

  the problem

  thepattern (i.e., the
solution of the problem)

  use cases for the pattern

  how the pattern applies for
the use cases
www.iks-project.eu

Page: 86

An Experiment within IKS:
Ambient Interaction Beyond Classical CMS

It's Thursday morning. I get site-specific weather information when I am
brushing my teeth in the bathroom. Based on weather information and my
calendar, free-time event suggestions are given, e.g. "Today, 8 p.m. - Miss
Marple Night at CinemaOne. Do you want to order tickets?”

Copyright by Duravit

Page: 87

Most of IKS Semantic CMS is used in the
AmI Case System

AmI Case System
Logical Architecture

IKS Semantic CMS
Architecture
The blue marked modules indicate modules that
exist in both architectures

Page: 88

Page: 31

License
IKS Licenses:
 IKS software is licensed under business-friendly open
source software licenses.
 IKS software can be freely used / changed / distributed
in your products.

 For the rare cases where artifacts use a less permissive
license, you will find a notice.
 e.g. we use models for natural language processing from
the Apache OpenNLP project whose licenses are not
clarified, yet.



Page: 89
32

Get in Contact

 VIE
 Homepage
http://viejs.org
 Google User Group
https://groups.google.com/forum/#!forum/viejs

 Apache Stanbol
 Homepage
http://incubator.apache.org/stanbol
 Mailinglist subscription
stanbol-dev-subscribe@incubator.apache.org


Page: 90

Thank you for your attention !

Acknowledgement:
to all participants of IKS,
especially the provider of in-depth tutorials.


Semantic
Technologies
for CMS
Semantic CMS Community

Dr. Tilman Becker
DFKI GmbH,
Saarbrücken, Germany

OpenCMS Days,
Cologne
September 25, 2012

Co-funded by the
91 Copyright Tilman Becker, DFKI
European Union

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