This document provides an overview and summary of a presentation about the PoolParty semantic platform. The presentation covers the following key points in 3 sentences: The presentation provides an overview of the PoolParty semantic platform and its components for creating and managing taxonomies and knowledge graphs, linking data sources, and integrating knowledge graphs into applications via API. It demonstrates how PoolParty allows unstructured and structured data to be combined by extracting entities from text based on knowledge graphs. The presentation highlights new features of PoolParty 5 including improved entity extraction, disambiguation capabilities, deep integration with content management systems, a web crawler to extract terms from websites, and expanded ontology management.

1. Five more reasons to
lean on a world-class
semantic platform
5

2. About this webinar
The power of
knowledge
graphs
Better Together:
Unstructured and
Structured Data
1
3
2
PoolParty
Platform:
Overview
4
PoolParty 5:
What’s new?
What’s next?
The Linked Data Path
5

3. About
Semantic Web Company founded in 2001
25 experts in linked data technologies
PoolParty Platform launched in 2009
Serving customers from all over the world
EU- & US-based consulting services
Andreas Blumauer, MScIT
CEO of Semantic Web Company
Find me/us on:
https://www.linkedin.
com/in/andreasblumauer
https://ablvienna.wordpress.
com/
http://www.slideshare.net/
semwebcompany
https://twitter.com/
semwebcompany

4. Our network: Customers & Partners
Some of our Customers
● Credit Suisse
● Boehringer Ingelheim
● Roche
● Wolters Kluwer
● BMJ Publishing Group
● Canadian Broadcasting Corporation (CBC)
● World Bank Group
● Inter-American Development Bank (IADB)
● International Atomic Energy Agency (IAEA)
● The Pokémon Company
● Healthdirect Australia
● Ministry of Finance (A)
● Wood Mackenzie
● Red Bull Media House
● Council of the E.U.
● TC Media
● American Physical Society
● Education Services Australia
● Pearson
● Norwegian Directorate of Immigration
● REEEP
● Techtarget
Finance / Automotive / Publisher / Health Care / Public Administration / Energy / Education
Selected Partners
● EBCONT
● EPAM Systems
● iQuest
● PwC
● Tenforce
● OpenLink Software
● Ontotext
● MarkLogic
● Gravity Zero
● Altotech
● Wolters Kluwer
● Taxonomy Strategies
● Digirati
We all have one goal in mind: Make machines smart
enough so that they can help us to find those needles
in the haystack, which are really relevant to us.

5. The power of
knowledge graphs.
1

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

9. Norway France Austria Canada
The power of knowledge graphs:
Agility, flexibility, complexity
doc doc doc
Europe,
Norway
EU,
Europe,
France
EU,
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
EU

10. 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

11. 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

12. Information integration:
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/

13. Semantic as a Service

14. 2
PoolParty Platform:
Overview

15. PoolParty core components
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.

16. See how it works:
PoolParty components & workflows
works on
basis for
● reference taxonomies
● linked data sources
● text reference corpora
enrich
basis for
Taxonomist/
Ontologist
Developer
● Confluence, WordPress
SharePoint, Drupal, ...
● search engine
● database
is user ofContent
Manager
enrich
annotate
basis for
analyzesuses API

17. PoolParty core components
Create and maintain
taxonomies and knowledge
graphs
“URIfication” of all types of things
Linking and mapping between
taxonomies and various graphs
Access and integrate knowledge
graphs via API (Read/Write)
Support of collaborative
workflows around taxonomy
management
RDFization of text
Extraction of ‘things’ (entities)
from text based on graphs
Extraction of (free) terms and
phrases from any kind of text
Categorization of documents
Ultra-fast auto completion based
on controlled vocabularies
Integrated with highly scalable
graph databases
Integration layer to improve
content and collaboration systems
Existing integrations with
SharePoint, Drupal, Confluence
Provision of user-dialogues to
support semi-automatic tagging
URI-based Semantic Indexing
Faceted search based on
taxonomies
Content recommender services

18. Better Together:
Unstructured and
Structured Data.3

19. Towards a Linked Data based search

20. Bringing structure to text:
PoolParty GraphSearch

21. PoolParty Enterprise Server + GraphSearch
System Architecture
Classified
documents +
Taxonomy
● Structured autocomplete
● Precise Search facets
● Dynamic topic pages
● Fully RDF compatible
● All queries via SPARQL
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22. Complex Queries based on 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.

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

24. 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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25. What’s new?
4
5

26. Five more reasons to get started with
PoolParty. Try it out now!
Get your PoolParty 5
Thesaurus Server &
Entity Extractor trial:
http://www.poolparty.biz/test-demo/

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

28. Why Disambiguation is important?
Example
Brian Johnson (born 5
October 1947) is an English
singer and songwriter.
Since 1980, he has been
the lead singer of Australian
rock band AC/DC, with
whom he was inducted into
the Rock and Roll Hall of
Fame in 2003.
Brian Johnson (born
December 7, 1990) is an
American professional
baseball pitcher in the
Boston Red Sox
organization. Johnson was
part of the No. 1 recruiting
class by Baseball America
at the University of Florida
Brian
Johnson?
Brian Johnson
Brian Johnson

29. Brian Johnson in the news
● http://www.pressherald.com/2015/02/08/on-baseball-low-flying-johnson-on-red-sox-radar/
● http://bosoxinjection.com/2015/01/24/red-sox-prospect-brian-johnson-undervalued-asset/
● http://www.blocku.com/2015/2/8/8002765/early-look-at-utah-footballs-2016-recruiting-class
● http://www.ksl.com/?nid=635&sid=33335577
● http://www.nydailynews.com/entertainment/music/ac-dc-singer-teleprompter-grammys-opening-article-1.2107723
● http://ultimateclassicrock.com/acdc-bon-scott-final-show/

30. Providing context in the knowledge graph

31. Activating disambiguation

32. Deep integration with
SharePoint, Drupal & Confluence
Semi-automatic tagging, semantic
search integration, content
recommendation - all based on
your own enterprise vocabularies!

33. Semantic Search integrated
with Confluence

34. PoolParty Integration with
Confluence Blueprints
⇒ Solution for Semantic
Records Management

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

36. Web Crawler extracts candidate terms
from any website

37. Refined look & feel
Refined look & feel improves user
experience and usability. Enjoy
working with PoolParty!

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

39. From Simple SKOS to
large knowledge graphs
your data,
e.g. Excel
your
docs
Generate 1st
version of SKOS
taxonomy
Edit,extend &
curate
taxonomy
Extend schema,
apply ontologies,
use SKOS-XL
Link and map
between
taxonomies
and LD graphs
- Taxonomy Editing
- Collaborative workflows
- Free term extraction
- Tag recommender
- Quality Checker
- Reuse of existing
vocabularies
- Corpus Analysis
- Excel import
- XML import
- Linked data harvester
your CMS
- Reuse existing ontologies
- Create custom schemes
- Apply SKOS-XL
- Apply ontologies on your
SKOS taxonomy
- Automatic mapping between
taxonomies
- Linked Data frontend
- Link to other LD graphs, e.g.
DBpedia or Geonames

40. What’s next - the “Linked Data Path”
1
Build capacities for internal
knowledge about linked data
4 Develop
Knowledge Models
2
Identify & understand strategic
dimension of linked data
3
Define crisp &
feasible use cases
5 Put aspect of 'reusability of
information' in the Centre
7 Implementation of the use
cases with focus on learning
curve of knowledge carrier
6
Focus on central
use case: integrated views
on business objects
8
Communicate the strategic
dimension
of linked data

41. Dive into your
Data Pools.