While Graph Databases have come of age, Data Warehousing seems to be broken in an increasing dynamic world. Are Graph Databases a smarter version of Data Lakes? In this webinar, Andreas Blumauer - CEO of Semantic Web Company discusses various approaches of data and information integration and the role knowledge graphs and taxonomies play in this game. Numerous organizations already discovered Enterprise Linked Data as a powerful solution for a 360-degree view on various business objects. But how do they solve the big challenge of connecting their data pools in heterogeneous and highly dynamic information landscapes? Learn more about the manifold application scenarios of linked data and semantic technologies. Dive into your data pools to gain new insights and knowledge!

1. Welcome to
this webinar!
Andreas Blumauer
CEO of Semantic Web Company

2. About Semantic Web Company (SWC)
SWC was founded 2001, head-quartered in Vienna
25 experts in Linked Data technologies
PoolParty Suite based on RDF Graph Data Model
Serving customers from all over the world
EU- & US-based consulting services

3. Our Ecosystem: Customers & Partners
Some of our Customers
● Credit Suisse
● Boehringer Ingelheim
● Roche
● adidas
● The Pokémon Company
● Canadian Broadcasting Corporation (CBC)
● Red Bull Media House
● Wolters Kluwer
● TC Media
● Techtarget
● BMJ Publishing Group
● CafePress
● Pearson - Always Learning
● Education Services Australia
● American Physical Society
● Healthdirect Australia
● World Bank Group
● Inter-American Development Bank (IADB)
● Renewable Energy Partnership
● Wood MacKenzie
● Development Initiatives
● International Atomic Energy Agency (IAEA)
Finance / Automotive / Publisher / Health Care / Public Administration / Energy / Education
Selected Partners
● PwC
● EPAM Systems
● iQuest
● EBCONT
● Gravity Zero
● MarkLogic
● OpenLink Software
● Ontotext
● Wolters Kluwer
● Data to Value
● Digirati
● Term Management
● Altotech
We are all working on the
replacement of data chaos
by networking information
● Norwegian Directorate
of Immigration
● Ministry of Finance (A)
● Council of the E.U.
● Australian National
Data Service

4. PoolParty Core Modules
Bain Capital is a venture capital
company based in Boston, MA.
Since inception it has invested in
hundreds of companies including
AMC Entertainment, Brookstone,
and Burger King. The company was
co-founded by Mitt Romney.
Taxonomy &
Ontology Server
Entity Extraction &
Text Mining
Semantic Search,
Analytics & Visualization

5. Why Graph
Databases?

6. The Enterprise Perspective:
The End of the Document
“Life is no longer as
simple as making
PDF documents.”
John Walker
Business Analyst at NXP Semiconductors

7. The Enterprise Perspective:
Graph Databases are Smart Data Lakes
“Data in a large cooperation is
often scattered over various tools,
comes in different formats and with
different levels of quality.”
Fabian Heinemann
Data Scientist at Roche

8. The NPO Perspective:
Using common Definitions and Standards
“Very few datasets
tell a story
in isolation.”
The Data Manifesto
Development Initiatives

9. The warehouse approach
seems to be broken in a complex world
Data Warehouse
- structures and categories predefine
the kind of analysis that is possible
- excludes data to simplify the data
model
- does not efficiently handle new
types of data
- supports efficient indexing
- enforces consistency
- includes all data that may be used and
even data that may never be used
- all data regardless of source and structure
is kept
- data kept in its raw form and only
transformed when used
- handles structured and unstructured data
- data models emerge with usage over time
Data Lake

10. The Analyst’s Perspective: Data Lakes don’
t fix the problem of lacking semantics
“Organizations should focus on
semantic consistency and
performance in upstream applications
and data stores instead of information
consolidation in a data lake.”
Gartner
Beware of the Data Lake Fallacy

11. Data Lakes have all the information to
answer complex queries, but….
Country GDP Pop
AUS 1,560 23.14
SVE 580 9.60
WITH A COMBINED NUMBER of
357,100 registered asylum claims
in 2013, Germany, the United
States of America, France, Sweden
and Turkey were the top five
receiving countries, together
accounting for nearly six out of ten
asylum claims submitted in the 44
industrialized countries covered by
this report.
Place Asylum
seekers
Year
Australia 24,300 2013
Sweden 54,300 2013
Show me all reports, in which EU member countries
are mentioned with regards to their asylum politics,
which have more than 10 asylum-seekers per 1,000
inhabitants.

12. ...taxonomies link constantly changing data
sources while analytic needs are evolving
Countries
European
Union
Sweden
SVE
France
FRA
Austria
AUT
Oceania
Country GDP Pop
AUS 1,560 23.14
SVE 580 9.60
Place Asylum
seekers
Year
Australia 24,300 2013
Sweden 54,300 2013
WITH A COMBINED NUMBER of
357,100 registered asylum claims
in 2013, Germany, the United
States of America, France, Sweden
and Turkey were the top five
receiving countries, together
accounting for nearly six out of ten
asylum claims submitted in the 44
industrialized countries covered by
this report.

13. Linked Data Warehouses are Smart Data Lakes
Data Lake
Data Warehouse
- supports efficient indexing
- enforces consistency
- handles structured &
unstructured data
- data models emerge
with usage over time
- standards-based
- unified data model
- powerful query language

14. What if questions
emerge when one
starts analyzing the
data?

15. The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Norway France Austria Canada
doc
Norway France Austria Canada
doc
Show me all
documents about
European countries
Traditional approach Graph-based approach
doc doc doc

16. The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
Europe,
France
Europe,
Austria
America,
Canada
doc
Norway France Austria Canada
doc
Show me all
documents about
European countries
Europe
Traditional approach Graph-based approach
doc doc doc

17. The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
Europe,
France
Europe,
Austria
America,
Canada
doc
Norway France Austria Canada
doc
Show me all
documents about
European countries
Europe
Traditional approach Graph-based approach
Show me all
documents about EU
member countries
doc doc doc

18. Norway France Austria Canada
The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
E.U,
Europe,
France
E.U,
Europe,
Austria
America,
Canada
doc doc doc doc doc
Show me all
documents about
European countries
Europe
Traditional approach Graph-based approach
Show me all
documents about EU
member countries
E.U

19. Norway France Austria Canada
The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
French,
EU,
Europe,
France
EU,
Europe,
Austria
French,
America,
Canada
doc doc doc doc doc
Show me all
documents about
European countries
Europe
Traditional approach Graph-based approach
Show me all
documents about EU
member countries
French-
speaking?
French-
speaking
EU

20. Norway France Austria Canada
The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
French,
EU,
Europe,
France
EU,
Europe,
Austria
French,
America,
Canada
doc doc doc doc doc
Show me all
documents about
European countries
Europe
Traditional approach Graph-based approach
Show me all
documents from EU
member countries
French-
speaking?
French-
speaking
EU
Metadata per
document
1. No or little network effects
2. No reuse of metadata
3. Metadata resides in silos
4. Data quality hard to measure
5. Not machine-readable
Knowledge about
metadata
1. Explicit knowledge models
2. Reusable and measurable
3. Metadata is machine-processable
4. Standards-based metadata
5. Linkable metadata opens silos

21. Better Together:
Unstructured and
Structured Data.

22. Towards a Linked Data based search

23. Bringing structure to text:
PoolParty GraphSearch

24. PoolParty GraphSearch =
Semantic Search + Analytics

25. Complex Queries based on
SPARQL and Linked Data
SELECT DISTINCT ?personname ?picture ?countryname ?hdi ?picture
WHERE
{
?person skos:prefLabel ?personname .
?country skos:prefLabel ?countryname .
?person a dbpedia:Person .
?country a dbpedia:Country .
?person skos:related ?country .
?country <http://dbpedia.org/property/hdi> ?hdi .
FILTER ( ?hdi < 0.6)
OPTIONAL
{
?person foaf:depiction ?picture .
}
} ORDER BY DESC(?hdi)
I want to explore medical
research trends in relation
to regional prosperity.

26. Organizing data in graphs using links
Graph nervous_system_diseases-abstracts
Graph en.dbpedia.org
Graph www.nlm.nih.gov/mesh
Graph www.geonames.org

27. PoolParty Semantic Integrator
System Architecture
Classified documents +
Linked taxonomies +
Knowledge graphs
● Dynamic filter criterias
● BI-like interface
● Large scale RDF store
● Fully RDF compatible
● All queries via SPARQL
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28. UnfiedViews as part of
PoolParty Semantic Integrator
UnifiedViews differs
from other ETL
frameworks by natively
supporting RDF data and
ontologies.
UnifiedViews has a
graphical user interface
for the administration,
debugging, and
monitoring of the ETL
process.

29. Use Cases

30. Success story: Healthdirect Australia
Over 120 information partners and sources
Great variety of category and metadata systems
One central vocabulary hub:
Australian Health Thesaurus (AHT)
Single point of access incl. harmonized search facets:
http://www.healthdirect.gov.au/

31. Clean Energy Data - Country Profiles

32. sOnr webMining for Confluence

33. Complex queries with SPARQL
PREFIX mrv-schema: <http://gbpn.org/mrv-schema/>
PREFIX qb: <http://purl.org/linked-data/cube#>
SELECT DISTINCT *
WHERE {
GRAPH <http://gbpn.org/mrv> {
?observation mrv-schema:year ?year.
?observation mrv-schema:region ?region.
?observation mrv-schema:region <http://gbpn.org/mrv-thes/region/India>.
?observation mrv-schema:scenario ?scenario.
?observation mrv-schema:scenario <http://gbpn.org/mrv-thes/scenario/deep-efficiency>.
{
?observation mrv-schema:urbanizationType ?urbanizationType.
?observation mrv-schema:urbanizationType <http://gbpn.org/mrv-thes/urbanization-type/urban>.
?observation mrv-schema:buildingType ?buildingType.
?observation mrv-schema:buildingType <http://gbpn.org/mrv-thes/building-type/MF>.
?observation mrv-schema:publicBuildingType ?publicBuildingType.
?observation mrv-schema:publicBuildingType <http://gbpn.org/mrv-thes/public-building-
type/NO>.
}
UNION
{
?observation mrv-schema:urbanizationType ?urbanizationType.
?observation mrv-schema:urbanizationType <http://gbpn.org/mrv-thes/urbanization-type/urban>.
?observation mrv-schema:buildingType ?buildingType.
?observation mrv-schema:buildingType <http://gbpn.org/mrv-thes/building-type/Slums>.
?observation mrv-schema:publicBuildingType ?publicBuildingType.
?observation mrv-schema:publicBuildingType <http://gbpn.org/mrv-thes/public-building-
type/NO>.
}
UNION
{
…….

34. More PoolParty Applications & Demos
Thesaurus Publishing Business Intelligence Content Recommendation Semantic Expert Finder
Web Mining Semantic Search Linked Data Visualization Symptom Checker

35. PoolParty 5.1

36. Highly precise entity extraction
Domain-specific extraction, highly
performant, language-agnostic,
disambiguation rules, REST API

37. Providing context in the knowledge graph

38. Activating disambiguation

39. Semantic Records Management: Integration
with Confluence Blueprints
⇒ Solution for Semantic
Records Management

40. Fully integrated web crawler
Make use of text corpus analysis:
Retrieve documents from various
sources, like RSS or from websites

41. Web Crawler extracts candidate terms
from any website

42. Extended ontology management &
semantic reasoning
From SKOS taxonomies to full-
blown ontologies:
PoolParty supports various levels of
knowledge modeling

43. Publishing custom schemes

44. Further extension of PoolParty API
● API method for skos:notes
● API method for skosxl:labels
● API methods for skos:collections
● API method to collect custom properties, attributes and types
● API method to R/W workflow status
● Retrieve history API method
● Retrieve SKOS subtree
Developer

45. Get started with PoolParty. Try it out now!
Get your PoolParty 5.1
Thesaurus Server &
Entity Extractor trial:
http://www.poolparty.biz/test-demo/

46. Contact points & further information
Andreas Blumauer, MSc IT
a.blumauer@semantic-web.at
https://www.linkedin.com/in/andreasblumauer
Semantic Web Company GmbH
Mariahilfer Strasse 70/8, A-1070 Vienna
+43-1-4021235
http://www.semantic-web.at
http://www.poolparty-software.com
Social Media Channels
http://slideshare.net/semwebcompany
http://youtube.com/semwebcompany
https://www.linkedin.com/groups?home=&gid=4059165