opencms days 2012 - keynote: semantic technologies for cms

Co-funded by the European Union

Semantic CMS Community

Semantic Technologies for CMS

September 25, 2012

Copyright Tilman Becker, DFKI 1

Dr. Tilman Becker DFKI GmbH, Saarbrücken, Germany OpenCMS Days, Cologne

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Web evolution

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Slide by Nova Spivack, Radar Networks

Web 1.0 Web 2.0

Web 3.0 Web 4.0

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What is the problem?

Tilman Becker, DFKI

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The Semantic Web  The vision of the Semantic Web has been originally

proposed by Tim Berners-Lee

  “The Semantic 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, ...)


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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

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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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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


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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:


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scheme authority path query fragment http://[email protected]:8042/over/there?name=ferret#nose

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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.


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Resource Description Framework (RDF)

  “The Resource 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.

 The Property value assigns a concrete value to a property, e.g. “Jaguar” or ““http://example.org/Jaguar”.

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RDF Statements  RDF statements consist of subject (resource), predicate

(property) and object (property value)

 Subjects (except Blank Nodes) and Predicates are

always defined by URIs  Objects can be defined by URIs and literals


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Subject Object (URI)

Predicate

Object (literal)

Predicate

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RDF Statements - Example  Exemplary statements:

  “The brand of the car is Jaguar.”   “The model of the car is XF.”


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

XF

http://example.org/rel/Model

Subject Predicate Object

Object

Predicate

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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

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


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Semantic Layer Web Cake


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Unique identification of resources

A format for specifying structured data in a machine-readable form

A model for describing resources with properties

and property values.

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RDF Queries  RDF provides a model for describing resources with

properties and property values.


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@prefix ex: <http://www.example.org/>. ex:Car1 ex:Brand ex:Jaguar ex:Car1 ex:Colour “Black” ex:Car2 ex:Brand ex:Jaguar ex:Car2 ex:Colour “White” ex:Car3 ex:Brand ex:VW ex:Car3 ex:Colour “Black”

How do I get all

black Jaguars?

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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


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Exemplary SPARQL Query “Return the models and prices for all cars of brand ‘Jaguar’ ”

SPARQL Query:

Exemplary Result:


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PREFIX ex: <http://example.org/> SELECT ?model ?price WHERE { ?car ex:Brand ex:Jaguar . ?car ex:Model ?model . ?car ex:Price ?price . }

Model Price “XJ” “79.750,00” “XF” “44.900,00”

Declares namespaces for abbreviated resources

identifiers.

Identifies the variables to appear in the query

results.

Provides the basic graph pattern to match against

the data graph.

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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, …)

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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


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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)


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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

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Searching for ontologies on the Semantic Web

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From the lessons learnt ...  Small ontologies 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.

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Ontology Design Patterns

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

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Align CMS Representation With External Ontology

-NewsSubjectCodes

-ArtsCultureEntertainment

-DisasterAccident

-EconomyBusinessFinance -Education

-EnvironmentalIssues

-Health

-HealthTreatment

-Illness

-Medicine

-SocialIssues

-Disease Representation of New Subject Codes as hierarchical ontology classes

-Obesity

-EatingDisorder

-MeSH

-Anatomy

-Diseases

-Organisms

-BehaviorMechanisms

-Psychiatry

-BehaviorDisciplines

-MentalDisorders

-AnxietyDisorders

-EatingDisorders

-SleepingDisorders

-SomotoformDisorders

Mesh Biomedical Ontology

equivalentTo

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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)


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OWL – Web Ontology Language

  “The OWL 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)


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

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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   Opens up great opportunities for logos   Owls are associated with wisdom   It has an interesting back story


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

http://piqs.de

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schema.org   “simple” ontology  Designed for web search

  Contains movies and records, but not plants and animals  Supported by

  Google   Bing   Yahoo!


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Back to the Cake ...


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Unique identification of resources

A format for specifying structured data in a machine-readable form

A model for describing resources with properties

and property values.

A language for describing a lightweight ontology.

A language for querying information specified in

RDF.

Highly expressive ontology language for modelling complex

knowledge domains.

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Linking Open Data Project   Is an 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

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Linked Datasets As of September 2010

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2011

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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)

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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

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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

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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.


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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>

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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>


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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>


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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


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NER Markup for a Web Page


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IE Template


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

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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


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NER Evaluation  Nobel Prize Corpus from NYT, BBC, CNN  538 documents (Ø 735 words/document)

  28948 person, 16948 organization occurrences


Sprout Calais Stanford NER

OpenNLP

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

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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


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web services




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IKS Goal

A Reference Architecture for Semantically Enabled Content Management Systems


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What is a Semantic CMS?


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vs. Traditional CMS

Atomic unit: Document Properties as meta-data e.g. author tags, keywords

Keyword search for strings in docs

Document Management Document types Document workflow

Semantic CMS

Atomic unit: Entity Semantic meta-data Defined entity types Linked entities

Semantic search for entities and their relations

Knowledge Management Entity management Ontologies


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What is a Semantic CMS?

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Building Semantic CMS  Ask the experts:  Top 8 CMS Customer Needs

  The following 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.


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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


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Ask the experts


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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

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IKS guidelines

 Do not change existing CMS!

 Provide as much abstraction as possible!


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Traditional CMS Architecture


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Semantic CMS Architecture


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Implementation of the Reference Architecture

 Reference implementation 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


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Do Not Replace – but Extend

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Do Not Replace – but Extend

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


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Traditional CMS

Database

IKS Technology

Stack

Extend by Using Semantic Services


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Use the Concepts of the Web

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Rely on the Concepts of the Web

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


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Traditional CMS

Database

IKS Technology

Stack

HTTP Request

HTTP Response


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IKS 7.0

Know

ledge Adm

inistration

Knowledge Repository

Knowledge Models

Reasoning

Semantic User Interaction

ContentRepository

Knowledge

IKSReference Implementation

Knowledge Extraction Pipelines

Content

SemanticUserInterface

Knowledge Access

ApacheStanbol

Reasoners

IKS VIEWidgets

IKS VIE

ApacheStanbol

Enhancer

ApacheStanbol Rules

ApacheStanbol

Ontology Manager

ApacheStanbol

ContentHub ApacheStanbol

EntityHub ApacheStanbol

FactStore

StanbolEnhancement

Engine

OSG

IConsole

ApacheStanbol

CMS AdapterCMIS /JCR

RDF

Apache Stanbol RESTful API

ApacheClerezza

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VIE Quick Facts

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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.


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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


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Apache Stanbol Service Layer

Apache StanbolComponent Layer

ApacheStanbol

Reasoners

ApacheStanbol

Enhancer

ApacheStanbol Rules

ApacheStanbol

Ontology Manager

ApacheStanbol

ContentHub

ApacheStanbol

EntityHub

ApacheStanbol

FactStoreStanbolEnhancement

Engines

VIE - User Interface LayerVIE VIE

Widgets

ApacheStanbol

CMS Adapter


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Service-Oriented View

Semantic Lifting Knowledge Representation & Reasoning

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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


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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


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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


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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


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Apache Stanbol Service Layer

Apache StanbolComponent Layer

ApacheStanbol

Reasoners

ApacheStanbol

Enhancer

ApacheStanbol Rules

ApacheStanbol

Ontology Manager

ApacheStanbol

ContentHub

ApacheStanbol

EntityHub

ApacheStanbol

FactStoreStanbolEnhancement

Engines

VIE - User Interface LayerVIE VIE

Widgets

ApacheStanbol

CMS Adapter

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VIE & VIE Widgets

Copyright IKS Consortium Semantic Lifting Knowledge Representation & Reasoning

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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


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VIE: Core

Javascript framework/library is a

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VIE: Core

Javascript framework/library

abstraction of

semantic entities and their relations

is a

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VIE: Core


abstraction of


is a

using

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VIE: Core


abstraction of


is a

addressing

using

Web Developers   bringing semantics into webpage   without caring too much about

triples/triplestores and so on

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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.

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VIE: UI Widgets

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.


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VIE Widgets

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

Widgets

Widgets


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It‘s about abstraction

VIE - UI Widgets „VIE-W“

VIE „Edit your content w. Semantics“

VIE-2 „Edit your Semantics“

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

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


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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") });

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Interaction Patterns: IP An IP consists of four parts:

  the problem   the pattern (i.e., the

solution of the problem)

  use cases for the pattern

  how the pattern applies for the use cases

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An Experiment within IKS: Ambient Interaction Beyond Classical CMS


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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

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


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IKS Semantic CMS Architecture

AmI Case System Logical Architecture

The blue marked modules indicate modules that exist in both architectures

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IKS Licenses:


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License

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.


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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

[email protected]


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Thank you for your attention !

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


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Co-funded by the European Union

Semantic CMS Community

Semantic Technologies for CMS

September 25, 2012

Copyright Tilman Becker, DFKI 91

Dr. Tilman Becker DFKI GmbH, Saarbrücken, Germany OpenCMS Days, Cologne

opencms days 2012 - keynote: semantic technologies for cms

Technology

eucopyright iks consortium

brand ex

jaguar ex

rdf statements rdf statements

semantic web thevision

separate web

web evolution

car3 ex