KESW2012 Linked Data for Enterprises and Governments (5 Oct 2012)

Linked Data @ KESW school
Knowledge Engineering and Semantic Web (KESW), 5 Oct 2012, St-Petersburg

Dr Sören Auer „Linked Open Data“

Senior scientist and head of the research group Agile Knowledge
Engineering and Semantic Web at University of Leipzig

Daniel Hladky, MBA „Enterprise Linked Data“

Researcher at NRU HSE “Semantic Lab”, Deputy Director W3C Russia Office
Board member at Ontos, Avicomp Services, Intecor, MatchCode Software

Agenda (morning)
Time Topic Speaker

10:00 Welcome, Intro and Objectives Daniel
Essentials and W3C View

10:15 Evolution of LOD Sören
Status Quo and Current Challenges

11:30 Break

12:00 LOD Lifecycle Sören

13:30 Lunch-Break

© AKSW (LOD2) – NRU HSE / W3C
Slide 2

Agenda (afternoon)

Time Topic Speaker

14:30 Linked Data for Enterprises Daniel
Use Cases

15:30 Hands-On LOD “Students”

16:00 Break

16:30 Hands-On continuation
17:30 Team presentation of hands-on
Wrap-Up Daniel

18:00 End
Slide 3

Objectives

• Understand the building blocks
– URI, RDF, RDFa, SPARQL …
• Know how to «Publish» and
«Consume» Linked Open Data
• Tools, use cases and references
• Understand benefits and
limitations

Slide 4

The Vision of the new Internet

Linked Data realizes the vision of
evolving the Web into a global
data commons, allowing
applications to operate on top of
an unbounded set of data
sources, via standardised
access mechanisms.

I expect that Linked Data will
enable a significant evolutionary
step in leading the Web to its
full potential.

CC-BY-SA von campuspartybrasil (flickr)

Slide 5

5 Stars for Open Data by Tim Berners Lee

Slide 6

W3C View

A new wave of transformations Working Groups (W3C Standards)
(http://www.w3.org/standards/semanticweb/data)
Just as the Web
has transformed - RDF, RDFa, SPARQL, RDB2RDF, OWL, RIF, SKOS
everything…

…It will transform
everything again

Slide 7

Some statistic

HTML/CSS Validation

Markup Validation
Slide 8

The Semantic Web is already there!

~30 bio. triples

http://bit.ly/d37p4i

Slide 9

Put the «L» in front of Open Data

Publish Data!
•Organise Data!
•License Data!
•Raw Data now!
Use Web-Technologies

•Provide an API!

• The web is an Ecosystem Use Linked Data!
• Networked Data creates
Network Effects • Give things an URI!
• Lowers Costs of Data • Use RDF for Publishing!
Integration • Link your Data to other Data
(as well as the data models)!
• Provide a Standard-API on top

Slide 10

Linked Open Data

Dr Sören Auer

11

LOD for Enterprise and Government

LINKED ENTERPRISE DATA
DANIEL HLADKY

HTTP://WWW.W3.ORG/2001/SW/SWEO/PUBLIC/USECASES/
HTTP://WWW.W3.ORG/2012/LDP/WIKI/USE_CASES_AND_REQUIREMENTS

Slide 12

What are Enterprise Data

Legacy (ERP) System CRM System

E-Mail (Outlook)
Wiki (MediaWiki)

CMS System

© AKSW (LOD2) – NRU HSE / W3C 13

Data managed in silos

Equipment
and assets Own schemas –
DB structures

Finance Student affairs

 Institutions, organizations and departments create and store their own data
 Departments do not effectively share information; they exchange data
 Data inconsistencies, redundancies, and errors affect business results and increase
costs

Slide 14

Connect the silos

Equipment & Assets

Enterprise-Wide Reusable
Information

Finance Student Affairs

Slide 15

Data Integration by SAP

SAP MDM
MDM
 Load master data from multiple transactional
systems (SAP & non-SAP) into a single, unified
EMPLOYEE PRODUCT SUPPLIER CUSTOMER
repository
 Identify and consolidate similar master data
values to eliminate duplicates
 Enrich master data values centrally for
enterprise wide purposes (such as reporting)

SAP BI (BW)
 Integrate data from any SAP or non-SAP data
source for analytics or business-transaction
processing
 Extract, transform, and load (ETL) data in
batch or real time

Slide 16

Next generation SAP Real-time
Data Platform and “EIM”

SAP SAP
SAP Big Data SAP SAP Custom
Business Business
3rd Party Applications Analytics Mobile Apps
Suite Warehouse
BI Client
SAP NetWeaver (On Premise / Cloud)

SAP Real-time Data Platform

Open Developer APIs and Protocols

Common Landscape Management
SAP Sybase SQLA
Sybase PowerDesigner

3rd Party DB
Scale-Out
Common Modeling

HADOOP
MPP

SAP Sybase ASE SAP HANA Platform SAP Sybase IQ

SAP Sybase ESP

SAP Sybase SAP Data
SAP MDG, MDM
Replication Server Services

SAP Smart Data Services Platform

Slide 17

Approach using LOD technology (W3C)

Slide 18

Linked Data in Enterprise Information Integration
Ref.: P. Frischmuth et al.

Slide 19

LED principles (or W3C LOD Cookbook)

Publishing Consuming LOD
• Analyse Data • Specify use cases
• Clean your Data • Evaluate relevant data
• Model your Data (Vocab.) sources and data sets
• Choose vocabularies • Check licenses
• Specify license(s) • Create consumption
• Convert to RDF patterns
• Link Data to other Data • Manage alignment
• Publish and promote • Create Mashup, GUIs,
serrvices and
applications on top
Slide 20

LED Best Practice - Vocabularies

• Prerequisites Linked Data Vocabs
– Terms must be referencable (e.g. via
URI)
– References have to be unambiguous
– Terms have to be mappable (maybe using
SKOS)
• Vocabularies (co-existence)
– UDEF, AGROVOC, folksonomies
(del.icio.us), Company Data Dictionaries
– Apply SKOS (W3C standard)

Slide 21

Example of Ontology/Vocab Repository

http://ontowiki.net/Projects/OntoWiki

http://protege.stanford.edu/

Slide 22

LED Best Practice – Data Curation

• The Business Need for Curation
– Complete, Accurate, Consistent, Provenance,
Timeliness
• Leads to a process:
> Identify data you need > Who will curate it >
Define curation process > Define tools, processes
needed to support the curation.
• How? Which Community approach:
– Internal (privat data)
– (External) Pre-competitive
– External – Crowd-sourcing

Slide 23

Data Curation Examples

• WikiPedia (crowd-sourcing) > DBPedia
• NYT Index (Started in 1913)

• Print «Index» once a year
– What about Online business?

Slide 24

NYT Index (Online)

WorkFlow at NYT (simplified)
1. Editor writes articles
2. Process article using autom.
Tagging (rNews) with NLP
3. Publish article online
4. Data curator review tagging and
correct manually
Slide 25

Demo of possible data curation process
RDFaCE PlugIn
- Various NLP
- RDFa in HTML
- rNews/schema.org
- RDF to EKB/IKB
- Data Curation

Ontos Framework

Slide 26

A possible framework (LED)
CRM Media- E-Gov Predictive
...
Int. News Eco(API) Analysis
Apps Eventos – Filter, Categorize, Visualise
Scalable Search in Linked Data

Manag. Quality & Extraction
Coherence
Base Technology

Knowledge

Triple Store
Co-
Linking Unstructured
User-Interface

Evolution
Scalability Semi-
Curation Matching
sructured

Orchas-
Data-Quality Structured
tration
Sources

Linked Docs
RDBMS Social
Op.Data (Org.Data)
(HTML) Networks

Slide 27

Tool Box (excerpt)

• W3C
– Guides and charters (http://www.w3.org/standards/semanticweb/data)
– Validator suite (http://www.w3.org/QA/Tools/)
• LOD2 Technology Stack
• Sindice Based on EU FPx
Often Open Source
• Silk
• LIMES
• NLP: OntosMiner, OpenCalais, GATE, UIMA
• RDF Store: Ontos, Virtuoso, AllegroGraph,
4Store
http://www.garshol.priv.no/blog/231.html
Slide 28

Early adopters

LED – USE CASES

Slide 29

Digital News and Semantics
Early adopters of RDF(a), SPARQL etc
– NYTIMES, BBC, Guardien, AP etc.


rNews (vocab/ontology)

RDF triple
subject – predicat - object
http://dev.iptc.org/rNews
Intro by Evan Sandhaus/NYT: http://vimeo.com/22891051
31

References to RDF(a)
http://www.w3.org/TR/2011/WD-rdfa-primer-
20110419/
http://www.w3.org/TR/rdfa-lite/

http://www.w3.org/TR/rdf-primer/

http://dev.iptc.org/Introduction-To-RDFa
Slide 32

rNews Guideline
Artikel
http://dev.iptc.org/rNews-Sample-Story

Guideline:
http://dev.iptc.org/rNews-10-Implementation-
Guide-Introduction

Using schema.org (namespace)
http://dev.iptc.org/rNews-10-Implementation-
Guide-HTML-5-Microdata

Using IPTC (namespace)
http://dev.iptc.org/Implementation-Guide-HTML-
5-Microdata-in-IPTC-namespace

Example
http://www.nytimes.com/2012/09/19/world/asia/n
ato-curbs-joint-operations-with-afghan-
troops.html?_r=3
Validation:
http://www.w3.org/RDF/Validator/
http://www.google.com/webmasters/tools/richsnippets
33

Demo RDFa (rNews)

http://hladky.ch/digipub/fake_news_html.html

http://dev.iptc.org/rNews-10-Implementation-Guide-HTML-5-Microdata

34

Why rNews

With structured
data
No structured
data

By understanding the structured data on a web page, search
engines can better present that web page to users.
Source: schema.org 2011

rNews markup allows you to describe the content on your site in a
machine-understandable way using RDFa.


Cash/Ringier


Cash Project
Objectives
• Similarity of articles
• Relevancy, Ranking
• SEO optimisation
• Metadata for MashUp


RIA Novosti

3

21 4
10 2 10 11
3 9 1 12 16 3
1 14
11 2
1 12
2
17 1
5

Slide 38

BBC – Dynamic Semantic Publishing

Slide 39

More from BBC

http://www.w3.org/2001/sw/sweo/public/UseCases/BBC/
http://www.bbc.co.uk/blogs/bbcinternet/2012/07/olympic_data_se
rvices_and_the.html
Slide 40

RDF(a) vs Schema.org by Google, Yahoo, BING, Yandex

http://schema.org/docs/schemas.html

Slide 41

Google Knowledge Graph

Slide 42

E-Commerce - GoodRelations
http://purl.org/goodrelations/

http://www.ebusiness-unibw.org/tools/goodrelations-
annotator/

Introduction by Dr M. Hepp from SemTech 2010
http://www.slideshare.net/mhepp/goodrelations-semtech2010-4590918

Slide 43

Magento Extension

http://www.heppnetz.de/ontologies/goodrelations/v1.html

http://www.magentocommerce.com/magento-connect/semantium/extension/2838/semantium_msemanticbasic#overview

Slide 44

LINKED DATA AT CAR COMPANY
Based on http://semantic-web-journal.net/content/linked-data-
enterprise-information-integration
http://semantic-web-journal.net/sites/default/files/swj300.pdf

Slide 45

LED at abc (Proof of Concept)
• The situation at abc:
• 3.000 heterogeneous IT systems
• Different units (car, bus, truck etc.) with very different
views
• No common language
• Inability to identify crucial entities (parts, locations etc.)
enterprise wide
• There is no (can not be a) single Enterprise Information Model
• A distributed, iterative, bottom-up integration approach
such as Linked Data might be able to help (pay-as-you-go).

Equipment & Assets

Enterprise-Wide
Reusable Information

Finance Student Affairs

Slide 46

Extraction from RDBMS
“SPARQLMap – Mapping RDB 2 RDF“

1.Either resulting RDF knowledge base is materialized in a triple
store &
2.subsequently queried using SPARQL
3.or the materialization step is avoided by dynamically mapping
an input SPAQRL query into a corresponding SQL query, which
renders exactly the same results as the SPARQL query being
executed against the materialized RDF dump

Slide 47

Data.gov / data.gov.uk / W3C LGD
Linked Government Data
W3C eGovernment Interest Group
http://www.w3.org/egov/wiki/Main_Page

Slide 48

What is Open (Government) Data?

Open Government Data is a worldwide movement
to open data (& information) of the government /
public administration* - that is NOT personal
(individual related) – in human- and maschine
readable open formats (non proprietary) for use & re
use!

OPEN stands for lowering the barriers to ensure as broad as
possible re-use (for everybody)!

There is a new paradigm in publishing Open Government Data
= look, take and play!

* ….. data and information produced or commissioned by government or government controlled entities


What is Important? For Whom?

What is important when thinking about open data in use?
•Interoperability to ensure broad & easy use & re-use
•Human AND machine readable data and meta data
•In open formats
•For smooth and cost efficient data integration
•To generate effects on several levels:
local – regional – national – EU wide & worldwide

For several target groups with several interests!
•Public administration (also for internal use)
•Politicians & decision makers
•Citizens (Citizen Analysts)
•Economy & Industry (data integration, -enrichment, APPs)
•(Data) Journalists, media & publishers
•Academia & Science


Data.gov (Open Data Sets) and Mashups

Civic Commons has a great collection of good open use cases:
http://civiccommons.org/

Slide 51

Where my money goes (Greece)

http://publicspending.medialab.ntua.gr/en/#/~/total
http://dl.dropbox.com/u/46182458/2012-06-19%20ps.gr%20BRU.pdf

Slide 52

E.g. Chicago - https://data.cityofchicago.org/

Slide 53

5 Star Pyramid of Open Data

http://5stardata.info/ (Dr M. Hausenblas, DERI)
See also:Christopher Gutteridge has a Linked Data crash course for
programmers. http://openorg.ecs.soton.ac.uk/wiki/Linked_Data_Basics_for_Techies

Slide 54

Let’s apply our knowledge

HANDS-ON

Slide 55

Example…..

https://www.dropbox.com/s/uzulsw3zu9eyff2/LOD_Test.zip

Slide 56

Wrap-Up: Benefits and Limitations

SUMMARY

Slide 57

Misconceptions about Linked Open Data

• All of us have to use ONE schema

• Everything needs to be switched to
RDF

• We all have to learn SPARQL, there
are no standard (web) APIs

• LOD is a pure academic approach

• LOD can only be used by Semantic
Web experts

• We have to change our data
integration & -management
approaches

Slide 58

The Power of Linked Open Data
• Enables web-scale data publishing - distributed publication with web-
based discovery mechanisms

• Everything is a resource – follow your nose to discover more about
properties, classes, or codes within a code list

• Everything can be annotated - make comments about observations,
data series, points on a map

• Easy to extend - create new properties as required, no need to plan
everything up-front

• Easy to merge - slot together RDF graphs, no need to worry about name
clashes

• Easy use and re-use on top of common schemas AND schema mapping

• Allows complex querying of several distributed data sources & systems

Slide 59

The Benefits of Linked Open Data

• Less replication (offering same
datasets in different places)

• Encouragement to re-use existing
datasets

• Clear which datasets are providing
similar / same information

• More innovation because datasets
can be put in a new context and
lead to interesting applications

• Put information in context and
thereby create knowledge

Slide 60

Cost of Data Integration – 2 Approaches

Can we afford to
mash the data with
ours?

Source: Price Waterhouse Coopers – Technology Forecast, Spring 2009

Slide 61

End of the Day (tomorrow hackathon for Open Gov Data)

Q&A

Slide 62

KESW2012 Linked Data for Enterprises and Governments (5 Oct 2012)

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