Download - Semantic Enterprise 2.0 - Enabling Semantic Web technologies in Enterprise 2.0 environment
Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
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Semantic Enterprise 2.0
EnablingSemantic Web technologies
in Enterprise 2.0 environment
Alexandre Passant, UldisBojars, John Breslin, Stefan Decker
Digital Enterprise Research Institute, National University of Ireland, Galway
Semantic Technologies Conference
15th June 2009
San José, USA
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Outline
Introductions and tutorial goals
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Speakers introduction
Alexandre Passant
Postdoctoral researcher, DERI NUI Galway; PhD thesis
“Semantic Web technologies for Enterprise 2.0”
UldisBojars
PhD student at DERI, NUI Galway, Co-founder of the
SIOC Project
John Breslin
Researcher at DERI, NUI Galway, Lecturer at College of
Electronic Engineering, Co-founder of the SIOC project
Stefan Decker
Director at DERI, NUI Galway
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Our lineage…
Memex (Vannevar Bush)
A memex is “a device in which an individual
stores all his books, records, and
communications.”
Augmenting Human Intellect
(Doug Engelbart)
“By „augmenting human intellect‟ we mean
increasing the capability of a man to approach a
complex problem situation, to gain
comprehension to suit his particular needs, and to
derive solutions to problems.”
WWW (Tim Berners-Lee)“There was a second part of the dream […] we could then use computers to help us analyse it, make sense of what we re doing, where we individually fit in, and how we can better work together.”
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A Network of Knowledge
Interconnected
Universal
All encompassing
Enable global and local collaboration
The right information for the right people at the right time
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Our Hypothesis…
Collaborative access to networked knowledge assists
humans, organisations and systems with their individual
as well as collective problem solving, creating solutions
to problems that were previously thought insolvable, and
enabling innovation and increased productivity on
individual, organisational and global levels.
Inspired by Doug Engelbart’s original
1962 report of: AUGMENTING
HUMAN INTELLECT: A
CONCEPTUAL FRAMEWORK
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Tutorial goals
What is Enterprise 2.0
Identify the shortcomings of current Enterprise 2.0
ecosystems
Explain how the Semantic Web can help to solve these
issues
Provide technical overview on how to implement a
Semantic Web architecture for Enterprise 2.0
Detail how to create, reuse, consume and mash-up RDF
data from several Enterprise 2.0 services
Discuss use-cases of such approaches
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Whatthis tutorial will not cover
Business process management and relationships with
Semantic Web technologies
Cloud computing, large-scale data management and the
Semantic Web
Natural Language Processing techniques to mine RDF
data from non-structured content
Talk to us if interested in this
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Outline
Introduction and tutorial goals
Overview and shortcomings of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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From the Web to a “Social Web”
The New Yorker, 1993
“On the Internet, nobody knows
you’re a dog.”
The New Yorker, 2005
“I had my own blog for a while,
but I decided to go back to just
pointless, incessant barking.”
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Features of Web 2.0 (O’Reilly)
1. The Web as platform
2. Harnessing collective intelligence
3. Data is the next “Intel Inside”
4. End of the software release cycle
5. Lightweight programming models
6. Software above the level of a single device
7. Rich user experiences
+ The long tail
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Web 2.0 in simple terms
1. Users
2. Content
3. Tags
4. Comments
Users post content
Users share content
Users annotate content with tags
Users browse content via tags
Users discuss content via comments
Users connect via posted content
Users connect directly to users
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Serious Applications for Web 2.0
Web 2.0 in researchenvironments
UsingWikis for projectproposals
Scientificblogging for communities (e.g. Nature network)
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Enterprise 2.0
Web 2.0 includes applications such as blogs, wikis, RSS feeds and social networking, while Enterprise 2.0 is the packaging of those technologies in both corporate IT and workplace environments
Corporate blogging
Corporate wikis
Social Networking inside organisations
etc.
“Enterprise 2.0 is the use of emergent social software platforms within companies, or between companies and their partners or customers”
Harvard Business School‟s Professor Andrew McAfee
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Enterprise 2.0 and the Web
Many enterprises got an online presence on Web 2.0
services to reach their customers
Twitter, Slideshare, Flickr, etc.
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The SLATES acronym
Andrew McAfee introduced the SLATES acronym to
identify the main features of Enterprise 2.0 systems
Search
– Information must be easily accessible for knowledge workers
Links
– Enable better browsing capabilities between content
Authoring
– Easy interfaces to produce content, in a collaborative way
Tagging
– User-generated classification, enables serendipity and knowledge
discovery
Extension
– Recommendation of relevant content
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Social aspects of Enterprise 2.0
Enterprise 2.0 introduces new paradigms in
organisations with regards to knowledge sharing and
communication patterns
The social aspect is as important than the technical
requirements
Enterprise 2.0 is a philosophy
Enterprise 2.0 success depends on a company‟s
background
A study by AIIM showed that 41% of companies do not have a
clear understanding of what Enterprise 2.0 is while this
percentage goes down to 15% in KM-oriented companies.
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Keys to Enterprise 2.0 adoption
Combining top-down and bottom-up approach helps to
realize Enterprise 2.0
Top-down: Hierarchy sets up new tools and requires various
services to use them
Bottom-up: Users become evangelists and word-of-mouth
improves the number of new users
“An adoption strategy for social software in enterprise”
– http://strange.corante.com/2006/03/05/an-adoption-strategy-for-
social-software-in-enterprise
“MiddleSpace” by Ross Mayfield‟s (SocialText)
– http://many.corante.com/archives/2004/10/27/middlespace.php
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Business metrics for Enterprise 2.0
13% of the Fortune 500 companies have a public blog
maintained by their employees
Forrester Research predicts a global market for
Enterprise 2.0 solutions of 4.6 billion dollars by 2013 and
according to Gartner , social computing platforms would
be adopted by companies in the next 10 years
Lots of companies and products in this space:
Awareness, Mentor Scout, Contact Networks, Microsoft SharePoint, IBM Lotus Connections, SelectMinds, introNetworks, Tacit, Illumio, Jive Software, Visible Path, Leverage Software, Web Crossing, SocialText
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introNetworks
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Jive Software
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Visible Path
Visible Path powers “Hoover‟s Connect
Lets users know how they're connected to companies and
people in the Hoover's database
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Open-source applications
Open-source Web 2.0 applications canbeefficientlyused
in organisations to build Enterprise 2.0 ecosystems
Blogging: WordPress, b2evolution, etc.
Wikis: MediaWiki, MoinMoin, etc.
RSS readers and APIs MagpieRSS, etc.
Integrated CMS: Drupal, etc.
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Information fragmentation issues
Heterogeneity of people, services, needs and practices
Impliesthatvarious services and tools are deployed
E.g. someuserswillprefer a wiki, another - a CMS, etc.
By usingvarious services (blogs, wikis, etc.) information
about a particularobject (e.g. a project) isfragmented
over the company‟s network
Getting a global pictureisdifficult
Applications act as independent data silos, withdifferent
APIs, different data formats, etc.
Data integrationcanbe a costlytask
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Lack of machine-readable data
Enterprise 2.0 enables and encourages people to
providevaluable content inside organisations
Especiallywikis, collaborative and open knowledge bases
Yet, information iscomplex to re-
use, generallylockedinside services and for human-
consumptiononly
Lack of (common) meta-databetween applications
Somequeriescannotbeansweredautomatically
« List all US-basedcompaniesinvolved in sustainableenergies »
« Whoisworking in company X for more than 6 years »
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Tagging issues
Taggingenablesuser-generated classification of content
withevolving and user-drivenvocabularies
Avoid to learnpre-defined taxonomies or controlledvocabularies,
simpler for end-users
Yet, itraisesvarious issues
Tag ambiguity
– « apple »: fruit or computer brand ?
Tag heterogeneity
– « Semtech », « semanticconference », « semtech09 »
Lack of organisation
– No links between the tags « SPARQL » and « RDF »
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Tagging issues –Use-case
EDF R&D – http://retd.edf.fr
> 3 years, 12257 tags, 21614 blog posts
54.2% of tags used only one time, 75.77% uses <= 3 times
Lots of valuable information lost in the long tail of tags
Tagging and expertise gap
194 items tagged with “TF” (= Thin Film)
– 1% of them tagged with “solar”
– < 0.5% of “solar” items tagged “TF”
Both tags are weakly related from a co-occurrence point of view,
clustering cannot be efficiently used
Valuable information gets lost !
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The long tail of tags
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Outline
Introduction and tutorial goals
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Enterprise 2.0: Semantics can help
By using agreed-upon semantic formats to describe people, teams, content objects and the connections that bind them all together, Enterprise 2.0 applications can interoperate by appealing to common semantics
Developers are already using semantic technologies to augment the ways in which they create, reuse, and link profiles and content on social media sites (using FOAF, XFN / hCard, SIOC, etc.)
Hence, it can be applied to extend existing architectures with simple and lightweight add-ons
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Semantic Web in Enterprise
Semantic Web technologies are already widely used in organisations:
Ontology-based information management
Semantic middleware between databases
Intelligent portals
Etc.
Semantic Web Education and Outreach (W3C)
http://www.w3.org/2001/sw/sweo/public/UseCases/
25 case-studies and 12 use-cases
NASA, Eli Lilly, Oracle, Yahoo!, Sun microsystems, etc.
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The (evolving) Semantic Web cake
http://www.w3.org/2007/03/layerCake.png
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Social Semantics ?
The Semantic Web and the Social Web are not disjoint
But can benefit of each other: Towards a Web of social and
interoperable data
Semantics for the Social Web
Using RDF(S)/OWL models to represent data from online
communities
Social interactions for the Semantic Web
Take advantage of social interactions to provide Semantic Web
data
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Synergies for Social Semantics
“I think we could have both Semantic Web technology supporting online communities, but at the same time also online communities can support Semantic Webdata by being the sources of people voluntarily connecting things together.”
Sir Tim Berners-Lee, podcast
interview during ISWC 2005
http://esw.w3.org/topic/IswcPodcast
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Social Semantic Information Spaces
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Semantic Enterprise 2.0 Architecture
Lightweight add-ons to existing applications to provide
RDF data
Exporters, wrappers, dedicated scripts …
Takingintoaccount the social aspect (e.g. semanticwikis)
Models to give meaning to this RDF data
Domain ontologies, taxonomies, etc.
Applications on the top of it
Thanks to RDF(S)/OWL and SPARQL
Most important, it does not require to rebuild IT
infrastructure but can be plugged on existing one !
Inspired by Tim Berners-Lee RDF Bus
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The RDF Bus approach
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Example architecture
http://www.w3.org/2001/sw/sw
eo/public/UseCases/EDF/
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Requirements
How to produce RDF data
Automatically and semi-automatically from existing applications
How to find, re-use and define ontologies
To model data contained in Enterprise 2.0 services
How to build interfaces on the top of it
Browsers, searchengines, mash-
upsthatprovideadvancedcapabilities
We will now go in detail into these topics
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Outline
Introduction and tutorial goals
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Data models for Enterprise 2.0
Twokinds of data models are required to
enableSemantic Enterprise 2.0
Models for the structure of communities and their social
interactions
FOAF – People, groups and enterprise social networks
SIOC –Semantically-Interlinked Online Communities–
Interactions within the community
Models of the content
Domain-specific ontologies
Taxonomies (SKOS)
Reusingexisting public RDF data (Linking Open Data)
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Ontology “Onion”
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FOAF (Friend-of-a-Friend)
FOAF is an ontology for describing people and the relationships that exist between them
Can be integrated with any other SW vocabularies
Widely used on the Web:
FOAF in Enterprise 2.0 settings
Model individuals, teams, interests / skills, etc
Allows one‟s identity to be modeled uniformly across various applications
Can be used for Expert finding
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Distributedidentitywith FOAF
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Social networking mining with FOAF
Can use FOAF to describe social networks in your
enterprise in a machine-readable way
Identify connections between people between various
applications and across departments
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SIOC
Semantically-Interlinked Online Communities
http://sioc-project.org
SIOC is an effort from DERI to discover how we can
create and establish ontologies on the Semantic Web
Goal of the SIOC ontology is to address interoperability issues
on the (Social) Web, both at a Web scale and in organizations
SIOC has been adopted in a framework of
50 applications or modules deployed
on over 400 sites
As well as corporate use-cases, more later
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Motivations for SIOC
Disconnected social websites require semantics and ontologies for interoperation:
Lots of social data, inherent semantics (chicken and egg)
Potential for high impact if widely adopted on the Web
In parallel, lack of integration between social software and other systems in enterprise intranets
Information fragmentation issue, as previously detailed
Enterprise 2.0 systems could be enhanced with semantics
Need to understand how to socially create and establish ontologies on the Web:
Social engineering for ontologies, in contrast to authoritative models
Model, agree, deploy, get feedback, re-model, etc.
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The steps involved
Develop an ontology of terms for representing rich
information about user-created content
Lightweight ontology, easily reusable
Create a food chain for producing, collecting and
consuming SIOC data
Open source applications to disseminate
As well implementation via industry and research
projects utilizing SIOC
An effort from both academics and industry
A constant feedback process to ensure we follow the needs of
people using it
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The SIOC ontology
The main classes and properties are:
SIOC Specification:
http://rdfs.org/sioc/spec
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The SIOC food chain
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Adoption of SIOC
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Using SIOC in Enterprise 2.0
Using SIOC as an ontology to represent the activities of Enterprise communities and their content
Represent wikis, blogs, microblogging platform, etc. with common semantics
Enables interoperability between different service providers on Enterprise 2.0 environments
Helps to solve the information fragmentation issue
As it eases the process of querying data from various sources within the Enterprise
Can be efficiently combined with other internal data (e.g. taxonomies or knowledge bases to represent discussion topics)
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Example of Enterprise 2.0 SIOC data
John wrote meeting minutes on his team blog, powered by Drupal:
:mypost rdf:type sioc:Post ;
dc:title “Meeting minutes” ;
sioc:has_creator :john ;
sioc:has_container :mydrupal .
:mydrupal rdf:type sioc:Forum .
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Using the SIOC Types module
For finer-grained content types, e.g. a wiki page:
:mypagerdf:typesioct:WikiPage;
dc:title “Understanding RDF” ;
sioc:has_creator :alex ;
sioc:has_container :mywiki.
:mywikirdf:typesioct:Wiki.
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Combining with external ontologies
To combine SIOC and other existing items, e.g. calendar information:
:event rdf:type ical:VEVENT;
dc:title “Next meeting on friday” ;
sioc:has_creator :uldis;
sioc:has_container :mycal.
:mycal rdf:type sioct:EventCalendar .
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Argumentative discussion and SIOC
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FOAF + SIOC = user and services
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FOAF + SIOC = Data Portability
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Models to represent content
A need for dedicatedmodels to represent content of blog
posts, wiki pages, etc.
Taxonomies and controlledvocabularies
Can beused to definesharetopicsacross applications
SKOS – Simple Knowledge Organisation System
http://www.w3.org/2004/02/skos/
Specificdomain ontologies
Check if existingmodelscan fit yourneeds
– Best practices documents, semanticsearchengines for ontologies
(e.g. Swoogle)
Extend if needed, republish to get public feedback
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Finding ontologies
How to Publish Linked Data on the Web
http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/
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Interlinking content with SKOS
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Models for Semantic Tagging (1)
Solving tagging issues thanks to semantics
Common modeling between applications
Linking to ontologies for further semantics
The “Tag Ontology” by Newman from 2005
tags:Tag rdfs:subClassOf skos:Concept
A “Tagging” class describes relationships between:– A user
– An annotated resource
– Some tags
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Models for Semantic Tagging (2)
SCOT (Social Semantic Cloud of Tags):
A model to describe tagclouds (tags and co-occurrence)
Ability to move your own tagcloud from one service to another
Share tagclouds between services, and between users
“Tag portability”
MOAT (Meaning of a Tag)
A model to define “meanings” of tags using existing URIs
e.g. SPARQL →http://dbpedia.org/resource/SPARQL
Tagged content enters the “Linked Data” web
Collaborative approach to share meanings in a community
– Servers can be installed in different departments / units
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Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Getting RDF data
Data about the services, their structure and the people
Can be completely automated thanks to RDF exporters for
FOAF and SIOC data
Data about the content, i.e. knowledge contained in
wikis, blogs, etc.
Generally requires end-user input
Semantic Wikis
NLP techniques can be considered
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Generating SIOC data
Exporters for major open-source applications
Drupal, WordPress, phpBB, b2evolution
To be included in Drupal7 core w/ RDFa !
Exporters for semi-structured data
IRC logs, .mbox files (SWAML project)
APIs to create your own exporters
Native applications exporting SIOC data
E.g. SMOB: Distributed and open semantic microblogging
http://smob.sioc-project.org
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SIOC export APIs
Benefits
Hides the complexity from application developers
Can be used by people who are not Semantic Web experts
Automatically updated according to changes in the SIOC ontology and best practices documents
Existing SIOC APIs:
Java
Perl (new!)
PHP (most used)
RDFa on Rails
See http://rdfs.org/sioc/applications/ (sec 2.1)
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Overview of existing SIOC exporters
There is a large amount of structured related
information contained within message boards, and this
can be leveraged in interesting ways by exposing the
semantic data for new applications
Exporters have been developed for commercial
(vBulletin) and open-source (phpBB) message board
systems, bringing these islands together and allowing
conversations on topics that are taking place across
various sites in a company
Based on the SIOC PHP API
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vBulletin SIOC exporter
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Issues withtraditionalwikis
Structured access
Information reuse
Made for humans,
not machines
Structured access:✗ Other books by JohnGrisham (navigation)✗ All authors that live in Europe? (query)Information reuse:✗ The authors from RandomHouse (views)✗ And what if I don't speak English? (translation)
JohnGrisham
He is the author of PelicanBrief.He lives in Mississippi.He writes a book each year.He is published by RandomHouse.
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Semanticwikis
Capture or identify further information about the pages in
a formal language, so that machines can (at least
partially) process and reason on it
Some systems focus on metadata about the content, some on
the social aspect, some on both
A semantic wiki could be able to capture that an article about
SPARQL related to Semantic Web and present you with further
related information
Various use-cases and prototypes
Some are used for personal knowledge management, others
aimed at KM for communities
http://www.semwiki.org/
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Fromwikis to Semanticwikis
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Structure and content
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SemPerWiki
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SemanticMediaWiki
An extension of MediaWiki:
Allows users to add structured data to the entries, turning it into a semantic wiki
Users can classify the “type” of links, e.g. making a relationship such as “capital of” between Berlin and Germany explicit:
– ... [[capital of::Germany]] ... resulting in the semantic statement "Berlin" "capital of" "Germany"
On the page about Berlin, users can explicitly define its population by writing:
– ... the population is [[population:=3,993,933]] ... resulting in the semantic statement "Berlin" "has population" "3993933"
Currently the most widely-deployed semantic wiki, Semantic MediaWiki is also being used by various organisations, and is being deployed as a service by Centiare and Wikia
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SemanticMediaWiki
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IkeWiki
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UfoWiki
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Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Browsing interfaces
Generic RDF / Linked Data browsers canbeused
Tabulator - http://www.w3.org/2005/ajar/tab
Disco - http://www4.wiwiss.fu-berlin.de/bizer/ng4j/disco/
SIOC/RDF browser - http://sparql.captsolo.net/browser/
etc.
Provides a uniformview for content
thatwasoriginallydesignedusingdifferent applications
Ensurehomogeneity of interfaces within a distributed information
system in Enterprise 2.0
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sparql.captsolo.net/browser
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Data integration and services
Distributed querying
Important to keep intact the decentralized architecture of the
ecosystem
Still experimental, can be quite slow
Central RDF storage
Ensure better performances
Using SPARQL to query data and to provide HTTP-based
interface on the top of the RDF store
– SPARQL is a W3C recommendations, current SPARQL WG
discusses evolution of the language
Lots of solutions on the market
OpenLink Virtuoso, Sesame, etc.
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Data integration and services
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Facet can be a direct or
indirect property:
Direct
The topic of the content item
The creator of the item
The date created
…
Indirect
A geographic location of the
person who created it
The gender of the person
An interest shared by many
creators
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Social SIOC Explorer
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The Sindice SIOC widget
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Example of advanced applications
The SIOC data competition
10 Years of SIOC data fromboards.ie made to public
http://sioc.me
Wewillalsocoversemanticmash-ups and other interfaces
in the Use-cases section
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Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
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Relational DB to RDF Mapping
Relational data (RDB) is structured data and can be
mapped to RDF straight-forward
Allows integration of existing enterprise databases into the
Semantic Enterprise 2.0 architecture
Main issues
Closed-world vs. open-world modeling
Assigning URIs for entities (records)
Mapping language expressivity
For a state-of-the-art see
http://www.w3.org/2005/Incubator/rdbrdf/RDB2RDF_
SurveyReport.pdf
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Relational DB to RDF Mapping
Standardization
W3C RDB2RDF Incubator Group 2008/2009
– http://www.w3.org/2005/Incubator/rdb2rdf/XGR-rdb2rdf-20090126/
Upcoming W3C RDB2RDF Working Group
Current solutions (see state-of-the-art)
D2RQ
– http://www4.wiwiss.fu-berlin.de/bizer/d2rq/
OpenLink‟s Virtuoso
– http://www.openlinksw.com/virtuoso/
Triplify
– http://triplify.org
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Linking Open Data
Lost of data available on the Web in data silos
Convertit in RDF, and interlink to enable network effect !
Linking Open Data
Communityprojectstarted in 2007 - http://linkeddata.org
Billion of triples nowavailable for free
Dbpedia, Geonames, riese (EuroStat in RDF)
BBC, Freebase, etc.
Raw Data Now !
SeeTimBL‟s TED talk
http://www.ted.com/index.php/talks/tim_berners_lee_on_the_nex
t_web.html
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The growing LOD cloud
100
2008
2007
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The growing LOD cloud
101
2009
2008
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LOD and Semantic Enterprise 2.0
Hugepotential for internal IT infrastructures to
enhanceexisting applications
Integration of open and structured data fromvarious sources
atminorcost
E.g. mashups (more later), extendeduser-interfaces, etc.
Issue: dependance on external services
Replicationmayberequired
RSS isalreadywidelyused in organisations as a way to
getinformation from the Web, LOD providesstructured
data to extend IT ecosystems
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Data discoverywithSindice
Sindice Search Engine
http://sindice.com
Look up by RDF by keywords and on property/value
descriptions
Simple queries but executed fast
Discover structured data on the Web to feed your IT system
Fast indexing (20 to 60m) of newly “pinged” information
Sindice can be thought as a “Spider In the middle” for application
2 application semantic communication via published data
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Data discoverywithSindice
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Re-using LOD in IT systems
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BBC music beta
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Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
Digital Enterprise Research Institute www.deri.ie
Semantic Enterprise 2.0 Use cases
Based on our own experience and projects during the
past few years
DERI Pergamon
Integrated framework for Enterprise 2.0
Electricité De France R&D
Integration of Enterprise 2.0 components using lightweight
semantics
Ecospace EU project
Interoperability of Collaborative Work Environments
European Space Agency
Integration of document repositories, databases and intranet
data
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Pergamon
Integrated framework for Enterprise 2.0
Blogs, wikis, microblogging ..
Named entity extraction and automatic tagging
User-profile consolidation
Visual interfaces to browse tag / people relationships
Based on FOAF and SIOC subsets
To model user-profile
Common models for different services
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Pergamon
Digital Enterprise Research Institute www.deri.ie
Pergamon
Digital Enterprise Research Institute www.deri.ie
Use-case: EDF R&D
EDF R&D: Blogs, wikis, RSS feeds
Extensions for data integration, enabling semantic mash-ups and
semantic search
Common semantics for various applications
SIOC and related vocabularies
Semantic wikis to maintain internal knowledge bases
Lightweight ontologies (SKOS, FOAF extensions …)
Tagging issues
Semantic tagging with MOAT
Features
Search engine, semantic mash-ups, faceted browsing
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Use-case: EDF R&D
SIOC exports for common semantics between
applications (blogs, wikis, RSS feeds)
Completely automated, full-transparency for end-user
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Use-case: EDF R&D
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Use-case: EDF R&D
UfoWiki
Specific wiki system including forms mapped to ontologies for
collaborative knowledge management
Live SPARQL-completion to ensure homogeneity of data
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Use-case: EDF R&D
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Use-case: EDF R&D
MOAT - Meaning Of A Tag
Specific MOAT client to create new mappings with tags and
instances from the internal knowledge base
Ability to create new instances via the same interface
User-interface for validation / disambiguation (if needed)
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Use-case: EDF R&D
Semantic search with end-user interface
RDF data stored in a central triple-store
Semantic search plugged on the top of it
Retrieving information from the different services
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Use-case: EDF R&D
Re-using RDF data from the LOD cloud internally
Mash-ups combining internal and external data
E.g. geolocation of wiki instances + faceted browsing
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Use-case: CWE Interoperability
The CWE Interoperability Architecture provides a middleware that enables
multiple, independent CWE platforms and third-party applications to share
and correlate data, based on SIOC.
CWESIOC
ExporterSIOC
Importer/ViewerCWE
Third-party Application
HTTP / Web Service
CWE Data
SIOC Data
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Use-case: CWE Interoperability
1. Concept Mapping: The first stage of translating proprietary CWE data into
SIOC RDF data involves mapping concepts that exist in a specific CWE
domain to concepts in the SIOC ontology.
2. SIOC Exporter: Based on the conceptual mappings, SIOC exporters translate
platform-specific data into SIOC RDF data.
3. Basic Collaborative Services (BCS): The BCS expose the content of a CWE
workspace as SIOC data to external systems. CWE items, such as documents
and folders, may be accessed, added, deleted, renamed, or replaced remotely
via these services.
4. SIOC Importer/Viewer: Importing remote SIOC data into a CWE allows a user
to view data from a remote SIOC RDF source as if it was a local folder in the
CWE. The SIOC Importer/Viewer reverts the SIOC data into CWE platform-
specific data, based on the conceptual mappings.
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Use-case: CWE Interoperability
Workspace Synchronisation
Sharing CWE files/folders between independent legacy CWE
platforms, e.g. BSCW, BC, SAP NetWeaver
SIOC Xplore Widget
Browsing across multiple, independent CWE platforms using a
single interface.
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Use-case: CWE Interoperability
BSCW Shadow Folder
BC Semantic Folder
private folders
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Use-case: CWE Interoperability
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Use-case: EuropeanSpaceAgency
Integration of Web 2.0 component withotherlegacy data
Translating intranet HTML into RDF
Integratingdatabaseswith D2RQ
Extractingmetadatafrom document repositorieswith Aperture
framework + RDF transformations
Storage and services
SIREn (engine behind Sindice, powered by SOLr / Lucene)
Triplestores for ranking / linkcounting
Features
Search engine with faceted browsing capabilities
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Use-case: EuropeanSpaceAgency
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Use-case: EuropeanSpaceAgency
Digital Enterprise Research Institute www.deri.ie
Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
Digital Enterprise Research Institute www.deri.ie
Conclusion
Enterprise 2.0 provides new communication means for
organisations, bothinternally and on the Web
Weblogs, wikis, microblogging, RSS feeds, etc.
Yet, itraisesvarious issues in terms of efficienlyusingthis
content
Lack of machine-readable data, information fragmentation,
tagging issues, etc.
Semanticscan help
Especiallylightweightsemantics
Can beapplied on the top of existing architectures
Provides new features for existing applications, and a potential
for new value-added applications
Digital Enterprise Research Institute www.deri.ie
Semantics for Enterprise 2.0
Digital Enterprise Research Institute www.deri.ie
Outline
Introduction
Overview of Enterprise 2.0
Solving Enterprise 2.0 issues with Semantics
Data models for Semantic Enterprise 2.0
Creating RDF data in Semantic Enterprise 2.0
Consuming Semantic Enterprise 2.0 data
Going further
Use cases
Conclusion
Digital Enterprise Research Institute www.deri.ie
Thankyou !
Contact us
http://deri.ie
Alexandre Passant
http://apassant.net
UldisBojars
http://captsolo.net
John Breslin
http://johnbreslin.com
Stefan Decker
http://www.stefandecker.org
Acknowledgements
Thanks to
ourcolleaguesDeirdre Lee,
Michael Hausenblas, Giovanni
Tummarello, Mark Leyden and
Bill McDaniel for input on the
slides
Workpresentedherehas been
funded in part by Science
Foundation Ireland under
Grant No. SFI/08/CE/I1380
(Lion-2)