Slidedeck for the final presentation of the Roadmap and Report

1. Austria's Roadmap for
Enterprise Linked Data

2. 14:45 … Hot Drinks
15:00 … Welcome
15:10 … The Project
15:20 … The Findings
15:35 … Conclusions, Roadmap
15:45 … Guest Presentation: Data Market Austria
16:00 … Get your Book and hand over to Vienna Open Data MeetUp

3. 3
The Austrian
Data Eco System

4. 4
The Austrian
Data Eco System

5. 5
The Austrian
Data Eco System
5

6. Sabrina Kirrane (WU, Privacy and Sustainable Computing Lab)
Julia Neuschmidt (IDC Austria)
Mihai Lupu (Researchstudios Austria)
Elmar Kiesling (TU, Linked Data Lab)
Thomas Thurner (SWC + School of Data)

7. The PROPEL Project
Propelling the Potential of Enterprise Linked Data
15.12.2016

8. PROPEL 8
 Emerging concept for data exchange and integration
 Based on standard web technologies
 Shifting away from a predominantly academic perspective, we
conceive Linked Data as a promising disruptive technology for
enterprise data management.
Source: blog.backand.com
Linked Data

9. The project goal
Survey industry and market needs, technological challenges,
and open research questions on the use of Linked Data in a
business context.
 FFG ICT of the Future 2014/2015
 Exploratory study
 Project duration Nov 2015 – Dec 2016
 Consortium: IDC Austria, Technical University of Vienna,
University of Economy Vienna, Semantic Web Company
PROPEL 9

10. Approach
PROPEL 10
Which industries are
the most likely to
adopt LD
technologies?
What are the key
drivers, inhibitors and
needs in data
management from a
demand side
perspective?

11. Approach
PROPEL 11
What
recommendations are
necessary for
enterprises, policy
makers and
researchers
in order to propel the
adoption of LD in
enterprises?
What are
technological and
standardisation
opportunities and
challenges?

12. Approach
PROPEL 12
Stakeholder Workshop Interviews Survey
respondents
Comprehensive Literature
research
Internal
workshops
www.linked-
data.at

13. Findings:
Sectoral Analysis of Linked Data Potential

14. Sectoral Analysis of LD Potential
Goal:
• Exploratory sectoral assessment of Linked Data adoption potential
• Alignment between Linked Data paradigm and industry characteristics
• Broad high-level, theoretical perspective
Methods:
• Industry classification: NACE rev. 2 top level sections,
with selective use of more detailed classes
• Extensive literature research
• Analysis of statistical data on industry characteristics
(R&D intensity, ICT spending,..)
• Industry expert interviews
• Internal validation survey
PROPEL 14

15. Working Hypotheses
Sectoral Characteristics → Adoption
15

16. Sectoral Characteristics - Results
16

17. High potential sectors
17
✅ Highly networked
✅ Strong (potential) impact
of ICT-based innovation
✅ Data- and ICT-intense
☑ Global scope
☑ Knowledge-intense
☑ Complex operations
☑ Relatively open
☑ Some uptake of web
technologies

18. Medium potential sectors
18
✅ Highly networked
✅ ICT- and data-intense
✅ Strong (potential) impact
of ICT-based innovation
☑ Highly internationalized
☑ Complex operations
❌ Have not embraced
openness
❌ Limited uptake of web
technologies

19. Lower potential sectors
PROPEL 19
✅ Structural characteristics
mostly favorable
❌ Moderate ICT dynamics
❌ Have not embraced
openness
❌ Trailing web technology
uptake

20. Results
 Broad potential for ELD across a large spectrum of industries
 Focus on ”openness” and “web-centric positioning” in
academic discussions may inhibit enterprise adoption
 Virtually all sectors in developed economies exhibit structural
characteristics that favor LD adoption:
• Actors in a highly networked global economy
• Increasingly data-driven and knowledge-intense
• Cross-organizational operations
 However, various sectors
• are laggards in the technological dimensions and
• have untapped potential for ICT-based innovation
PROPEL 20

21. Findings:
Market Forces

22. Market Forces
PROPEL 22
 Economy:
• Positive economic development in Austria leads to a growth in IT spending and
we expect investments solutions for data and information management
 Efficiency:
• Organizations focus primarily on costs. Data and information management
solutions and LD can have positive effects in terms of transforming businesses,
increasing efficiency and driving growth
 Digital Transformation:
• Data and information management is a key asset for digital transformation, and
concepts around Linked Data can support the transformation process

23. Market Forces
PROPEL 23
 Culture:
• Missing innovation culture in some organisations might be inhibitors for the uptake
of new technologies
 Data driven networked global economy:
• Growing need to break up silos, and to share data across organizational
boundaries.
 Digital life of citizens:
• High Internet adoption and user demands for new digital products and services
lead to redefinition and expansion of services.

24. Market Forces
PROPEL 24
 Technology:
• New technologies like cloud, big data, IoT and cognitive computing/machine
learning change the way our data is managed.
 Data security and privacy:
• Common barriers to adoption of new technology; at the same time security
concerns provide an opportunity for solution providers to generate revenue out of
their security solutions and services.
 Regulations:
• General Data Protection Regulation forces organizations to take a fresh look on
how they manage their data.

25. Big efforts for data and information management
PROPEL 25
Demand-side analysis

26. PROPEL 26
Demand-side analysis

27. PROPEL 27
Demand-side analysis

28. Findings:
Technology

29. Interviews
23 interviews:
 Domains
 Consulting, Engineering, Environment, Finance and Insurance,
Government, Healthcare, ICT, IT, Media, Pharmaceutical, Professional
Services, Real Estate, Research, Startup, Tourism, Transports & Logistics
 Roles
 Business Intelligence, CEO, Chief Engineer, Data and Systems Architect,
Data Scientist, Director Information Management, Enterprise Architect,
Founder, General Secretary, Governance, Risk & Compliance Manager,
Head of Communications and Media, Head of Development, Head of HR,
Head of R&D, Innovation Manager, Information Architect, IT Project
Manager, Management, Managing director, Marketing Analyst, Principle
System Analyst, Project Coordinator, Researcher, Technical Specialist
PROPEL 29
Note: Instead of explaining them what
ELD is, we gathered their
technology/research expectations from
a more general SW perspective

30. Technologies in need…
PROPEL 30
Analytics
Computational
linguistics & NLP
Concept tagging &
annotation
Data integration
Data management
Dynamic data /
streaming
Extraction, data
mining, text mining,
entity extraction
Logic, formal
languages &
reasoning
Human-Computer
Interaction &
visualization
Knowledge
representation
Machine learning
Ontology/thesaurus
/taxonomy
management
Quality &
Provenance
Recommendations
Robustness,
scalability,
optimization and
performance
Searching,
browsing &
exploration
Security and
privacy
System engineering

31.  Monitoring SW community major venues:
• ISWC (since 2006), ESWC (since 2006), SEMANTiCS
(since 2007), JWS (since 2006), SWJ (since 2010)
 3 seminal papers:
PROPEL 31
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Community Analysis

32. Topic Categorisation
PROPEL 32

33. Semantic Web/Linked Data over time…
PROPEL 33
Subtopics:
Expressing Meaning
Knowledge Representation
Ontologies
Agents
Evolution of Knowledge

34. Knowledge Representation
& Reasoning
PROPEL 34

35. Semantic Web/Linked Data over time…
PROPEL 35
Early adopters:
MITRE
Chevron
British Telecom
Boeing
Ordnance Survey
Eli Lily
Pfizer
Agfa
Food and Drug Administration
National Institutes of Health
Software adopters/products:
Oracle
Adobe
Altova
OpenLink
TopQuadrant
Software AG
Aduna Software
Protége
SAPHIRE

36. LD Adopters - Companies
PROPEL 36

37. LD Adopters - Companies
PROPEL 37
0
200
400
600
800
1000
1200
1400
1600
Google Oracle Yahoo SAP IEEE
Intelligent
Systems
Franz Bing Expert
System
IBM Research Poolparty
Occurrences
Companies
Conference Sponsors that appear in papers 2006-2015

38. PROPEL 38

39. Semantic Web/Linked Data over time…
PROPEL 39
The authors claim that "early research has
transitioned into these larger, more
applied systems, today’s Semantic Web
research is changing: It builds on the
earlier foundations but it has generated a
more diverse set of pursuits”.

40. Looking to the future
PROPEL 40

41. ROADMAP

42. Roadmap Formulation
1. Austrian perspective SWOT analysis:
1. Awareness and education
2. Technological Innovation and Research
3. Standardization
4. Legal and Policy
5. Funding
2. Development of prioritized recommendations
PROPEL 42

43. SWOT
43

44. Know the threats
44

45. See the weaknesses
45

46. Build on strength
46

47. Take the opportunities
47

48. Activities
54
Long-term
Support
emerging Linked
Enterprise Data
ecosystems
Establish
centers of
excellence
Position Austria
as a hotspot for
LED research
and innovation
Awareness and
Education
Legal and
Policy
FundingTechnological Innovation
Research
Medium-term
Develop key
foundational
technologies
Institutional and technological focus
on key issues and domains
Short-term
Cluster
stakeholders
and efforts
Get momentum
from new
funding lines
Supporting
studies and pilot
projects

49. Take-home messages
PROPEL 55

50. "Use the power of ELD!"
 Many industries are facing disruptive change
 Even conservative industries see a need for a "two speed IT"
 Linked Data can be both a disruptive force and a means to
respond to disruptive change
 Key ELD technologies are mature and have been successfully
applied in many domains
 Linked Data is agile and flexible
 ELD is a enabler for product, process and business model
innovation!
PROPEL 56

51. "ELD is the backbone for the
developing content industry"
 Linked Data is particularly relevant for online businesses
(media, e-commerce, etc.)
 ELD provides a platform to generate and leverage economic
network effects typical for these industries
 Tools to enrich digital products and make them
interchangeable within a broader digital environment
PROPEL 57

52. "We need to align research
priorities and practical needs"
Continued fundamental basic research necessary, but:
 Industry needs should be reflected in applied
research agendas
 More courage to apply cutting-edge technologies in
industry needed!
PROPEL 58

53. "ELD has to convince stakeholders
to embrace change"
 Technological, behavioural and cultural adoption barriers
 New skill sets required
To instigate change, ELD must
 ..make sense from a business perspective
→ clear business cases, fast returns, tangible, quantifiable benefits
 ..lower entry barriers
• by playing well with existing infrastructure
• through open source/freemium/experimental models
 ..address security, privacy, and compliance concerns
PROPEL 59

54. "Need to support [and subsidize]
emerging ELD ecosystems"
 Prototypical example of a technology with strong economic network effects
 Flagship implementations and pioneering projects are key to furthering the
growth of ELD in Austria.
 Both financial and infrastructural support are necessary in order to
accelerate the development of the sector.
Core preparatory steps include:
• Base infrastructures (stores, services, data) to build solutions on top
• Project related funding
PROPEL 61

55. Julia Neuschmid | jneuschmid@idc.com
PROPEL 62
Thank you!
www.linked-data.at

56. Backup

57. Linked Data in a Nutshell
PROPEL Workshop
May 10, 2016

58. Linked Data from 10,000 foot...
• Best practices for publishing and connecting
structured data on the Web
• Goal: Creating a global data space
Web of Documents Web of Data

59. ... and up-close
 Graph-based data model that captures statements about
things in the world
 Subject-predicate-object triples
 Use of URIs as globally unique identifiers
PROPEL
66
http://example.com/
alice
http://xmlns.com/foaf/0.1/knows
http://example.com/
bob
:alice
foaf:knows
:bob

60. Principles
Anyone can…
• publish data
• create URIs
• choose or create vocabularies to represent their data
• refer to Linked Data published by others
Result:
• Decentralized data infrastructure (> 650.000 datasets)
• Machine-readable, and -discoverable data sets
• Bottom-up "pay as you go" data integration
PROPEL 67

61. Key ideas
PROPEL 68
Explicit SemanticsWeb of data Graph-based
Network effects
Global data space
Bottom-up
Flexible
Agile Machine readable
Interoperable
Ad-hoc integration
Linking
Decentralized Inference
Discovery
a
b
cx
y
Emergent
Open

62. Evolution of the Linked Data Cloud: 2007
http://lod-cloud.net

63. Evolution of the Linked Data Cloud: 2008
http://lod-cloud.net

64. Evolution of the Linked Data Cloud: 2009
http://lod-cloud.net

65. Evolution of the Linked Data Cloud: 2010
http://lod-cloud.net

66. Evolution of the Linked Data Cloud: 2011
http://lod-cloud.net

67. Media
Geographic
Publications
Social
Networking
Government
Cross-
Domain
Life
Sciences
User
Generated
Content
Linguistics
Evolution of the Linked Data Cloud: 2014
http://lod-cloud.net

68. ELD and LED
Enterprise Linked Data:
Internal use of LD technologies within organizations, e.g.,
• to integrate heterogeneous systems at the data level
• for advanced content/knowledge/… management
• as a basis for innovative products and services
Linked Enterprise Data:
• Cross-organizational data integration
• Data markets and data ecosystems
• Decentralized infrastructure for a networked economy
PROPEL 75

69. What's the difference between
Linked Data and... ?
PROPEL 76

70. Linked Data vs. Open Data
Overlaps:
• Openness is a core principle in the design of LD
• Many Linked Data sets published under an open license
→ Linked Open Data and LD often used interchangeably
Key differences:
• Linked Data technologies can be used without publishing data –
e.g., for internal and external data integration.
• Not all open data will ever be linked (the majority will remain in
formats such as csv, txt etc.)
PROPEL 77

71. Linked Data vs. “The” Semantic Web
Overlaps:
• "LD is the Semantic Web done right" (Tim Berners-Lee)
• Semantic web is made up of Linked Data.
• Linked Data is based on Semantic web standards.
Key Differences:
• Semantic Web was all about "semantifying" the web, Linked Data is
based on web standards (URIs, http), but doesn't center around web
pages.
• LD is a more pragmatic "bottom-up" approach.
• "Linked Data is mainly about publishing structured data in RDF using URIs
rather than focusing on the ontological level or inference."
M. Hausenblas "Exploiting Linked Data For Building Web Applications"
IEEE Internet Computing, 2009

72. Linked Data vs Big Data
Overlaps:
• LD as a whole is big ( *)
• No rigid up-front (e.g., relational) data model
• Big Data technologies (e.g., Hadoop) are used to handle LD
• LD can represent knowledge extracted from big unstructured data
Key Differences:
• Individual linked data sets are typically not "big" per se
(e.g., English DBPedia dump currently < 5 GB)
• LD is structured and semantically explicit,
"big data lakes" are typically neither
• Big data based on distributed data infrastructures within an organization
(e.g., Hadoop clusters), LD creates a decentralized, globally distributed data
infrastructure
PROPEL 79
http://lodlaundromat.orgasper2016-05-10

73. Linked Data vs Knowledge Graphs
PROPEL 80
Facebook Open Graph Google's knowledge graph
Examples:

74. Linked Data vs Knowledge Graphs
Overlaps:
• Knowledge Graphs also represent explicit semantics in a
graph-based data model
• Both are often used to facilitate semantic search
• Knowledge graphs can use open standards (e.g., RDFa)
Key differences:
• Proprietary (data and technologies), closed "ecosystem"
• Tightly integrated with services
• Typically not published externally → no way to link to

75. References
Videos:
 Tim Berners-Lee: The next Web of open, linked data (16:52)
 Linked Data (and the Web of Data)
 Manu Sporny: What is Linked Data (12:09)
 Michael Hausenblas: Quick Linked Data Intro (3:14)
 Annenberg Networks Theory Seminar with Tim-Berners-Lee
 Metaweb (now defunct): Words vs entities
Tutorial:
 Linda Project: Linked Data Primer
Articles:
 C. Bizer, T. Heath, and T. Berners-Lee. Linked Data - The Story So Far. International Journal on
Semantic Web and Information Systems, 5(3):1 – 22, 2009.
Books:
 T. Pellegrini, H. Sack, and S. Auer, Eds., Linked Enterprise Data. Heidelberg: Springer Berlin, 2014.
 Tom Heath, Christian Bizer (2011). Linked Data - Evolving the Web into a Global Data Space.
Morgan & Claypool, 2011.
 EUCLID Project Consortium (2014). Using Linked Data Effectively.
 Hitzler, Rudolph, Krötzsch (2009). Foundations of Semantic Web Technologies. Chapman & Hall/CRC
PROPEL 82

76. Linked Data Principles
1. Use URIs to identify things
2. Use HTTP URIs so that people can look up
those names
3. When someone looks up a URI, provide
useful information, using the standards
(RDF, SPARQL)
4. Include links to other URIs so that they can
discover more things
DesignIssues:LinkedDatanotes,TimBerners-
Lee

77. The Semantic Web Technology Stack
http://bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake

78. Selected Linked Data
Standards/Technologies
 URIs + HTTP:
• Web infrastructure that provides global identifiers for all objects
 RDF:
• provides a generic graph-based data model for describing things
• various serializations
 RDFS and OWL
• Basis for the definition of vocabularies
(i.e., collections of classes and properties)
• Expressed in RDF
• Facilitates inference (using reasoning engines)
 SPARQL:
• Graph pattern-based query language (and protocol) for RDF data
PROPEL 85

79. Vocabularies
 Many vocabularies beyond those defined in the RDF standard
 Collections of defined relationships and classes of resources
 Vocabulary definition and reuse is a key semantic web principle
Adapted from Euclid learning materials by Barry Norton
Best practices:
• Terms from well-known vocabularies
should be reused wherever possible
• New terms should be defined only if you
can not find required terms in existing
vocabularies
• Feel free to mix terms from different
vocabularies and to extend the
vocabularies with additional terms in
your own namespace

80. Examples of common Vocabularies
Vocabulary Description Classes and Relationships
Friend-of-a-Friend
(FOAF)
Vocabulary for describing
people.
foaf:Person, foaf:Agent,
foaf:name, foaf:knows,
foaf:member
Dublin Core (DC) Defines general metadata
attributes.
dc:FileFormat, dc:MediaType,
dc:creator, dc:description
Semantically-Interlinked
Online Communities
(SIOC)
Vocabulary for representing
online communities.
sioc:Community, sioc:Forum,
sioc:Post, sioc:follows,
sioc:topic
Music Ontology (MO) Provides terms for
describing artists, albums
and tracks.
mo:MusicArtist,
mo:MusicGroup, mo:Signal,
mo:member, mo:record
Simple Knowledge
Organization System
(SKOS)
Vocabulary for representing
taxonomies and loosely
structured knowledge.
skos:Concept, skos:inScheme,
skos:definition, skos:example
Adapted from Euclid learning materials by Barry Norton

81. Linked Data from an Application
Development Perspective
 Data is self-describing (applications can dereference
URIs that identify vocabulary terms in order to find
their definition)
 Use of HTTP as standardized data access
mechanism and RDF as a standardized data model
simplifies data access compared to Web APIs,
which rely on heterogeneous data models and
access interfaces
 Web of Data is open, i.e., applications do not have
to be implemented against a fixed set of data
sources, but can discover new data sources at run-
time by following RDF links.
PROPEL 88