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Andreas Blumauer
CEO, Semantic Web Company
Helmut Nagy
COO, Semantic Web Company
POOLPARTY
SEMANTIC
SUITE
FUNCTIONAL
OVERVIEW
1
6.0
INTRODUCTION
2
Semantic Web
Company
founder &
CEO of
Andreas
Blumauer
developer and
vendor of
2004
founded
6.0
current
Version
active at
based on
Vienna
located
part of
Taxonomies
Knowledge
Graphs
manages
standard for part of
is a
>200serves customers
INTRODUCING
SEMANTIC WEB
COMPANY
Semantic Web Company (SWC)
▸ Founded in 2004
▸ Based in Vienna
▸ Privately held
▸ 40+ employees, experts in text
mining & linked data
▸ ~15-20% revenue growth
per year
▸ 2.5 Mio Euro funding for R&D
▸ SWC named to KMWorld’s
2016 and 2017 ‘100
Companies That Matter in
Knowledge Management’
▸ https://www.semantic-web.com
3
INTRODUCING
POOLPARTY
PoolParty Semantic Suite
▸ First release in 2009
▸ Current version 6.0
▸ W3C standards compliant
▸ Over 200 installations
world-wide
▸ 50% of revenue is reinvested
into PoolParty development
PoolParty on-premises or
used as a cloud service
▸ KMWorld listed PoolParty as
Trend-Setting Product 2015
and 2016
▸ https://www.poolparty.biz/
4
SELECTED
CUSTOMER
REFERENCES
AND PARTNERS
SWC head-
quarters
5
Customer References
● Credit Suisse
● Boehringer Ingelheim
● Roche
● adidas
● The Pokémon Company
● Canadian Broadcasting Corporation
● Harvard Business School
● Wolters Kluwer
● Talend
● HealthStream
● TC Media
● Techtarget
● Seek
● CafePress
● Pearson - Always Learning
● Education Services Australia
● American Physical Society
● Healthdirect Australia
● World Bank Group
● Inter-American Development Bank
● Renewable Energy Partnership
● Wood MacKenzie
● Oxford University Press
● International Atomic Energy Agency
● Norwegian Directorate of Immigration
● Ministry of Finance (AT)
● Council of the E.U.
● Australian National Data Service
Partners
● Accenture
● EPAM Systems
● Enterprise Knowledge
● Mekon Intelligent Content Solutions
● B-S-S Business Software Solutions
● MarkLogic
● Wolters Kluwer
● Digirati
● Quark
US
East
US
West
AUS/
NZL
UK
TECHNICAL
CORE
COMPONENTS
6
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 Extractor &
Text Mining
Data Integration &
Data Linking
Unstructured
Data
Semi-
structured
Data
Structured
Data
Unified
Views
PoolParty
GraphSearch
Identify new
candidate concepts
to be included in a
controlled vocabulary
Controlled vocabularies
as a basis for highly
precise entity
extraction
Entity Extractor informs
all incoming data
streams about its
semantics and links them
Schema mapping
based on ontologies
RDF
Graph Database
PoolParty as a
supervised
learning
system
7 Content Manager
Integrator
Taxonomist/
Ontologist
Thesaurus
Server
Extractor
PowerTagging
uses API
is user of
is user of
is basis of
is basis of
Index
annotates
enriches
Corpus Learning/
Semantic Analysis
CMS
extends
is basis of
analyzes
uses API
proposes
extensions
POOLPARTY 6.0
Benefit From
New Functions and Features
8
Place your screenshot here
9Semantic Middleware
Configurator
Use PoolParty as the control center
for your linked data management!
Configure and connect to available
indexing engines and graph
databases.
Set up available linked data sources
and visualization tools in one place.
Place your screenshot here
10Handling of Large
Reference Text
Corpora
Analyze thousands of documents
and extend your knowledge model
semi-automatically.
Let machines learn from text and
benefit from high-quality
taxonomies.
Corpus analysis
results in a
network of
concepts and
terms
11
I need support to
continuously extend our
taxonomy / controlled
vocabulary!
skos:
Concept
Reference
Corpus
- Websites
- PDF, Word, …
- Abstracts from
DBpedia
- RSS Feeds
skos:
Concept
skos:
Concept
Term 1
Term 3
Term 7
Term 8
Term 6
Term 4
Term 2
Term 5
- Relevant terms and phrases
- Relevancy of concepts
- co-occurence between concepts and terms
- co-occurence between terms and terms
Place your screenshot here
12Shadow Concept
Extraction
Benefit from deep semantic analytics
of your content.
PoolParty now even extracts implicit
knowledge from your texts.
Make use of corpus learning and
statistical language models
combined with semantic knowledge
graphs.
Use co-occurences
between concepts
and terms to
extract ‘shadow
concepts’
13 Inca
site
Machu
Picchu
Cusco
Inca
empire
Inca
emperor
Peru
Spanish
Conquest
Sacred
Valley
Chankas
Lost
City
Pachacuti
Machu Picchu is not mentioned explicitly in the article. But it is
mentioned indirectly via its co-occuring terms. By these means,
PoolParty can suggest it as a Shadow Concept for annotation.
Example:
This site is a
15th-century Inca
site located 2,430
metres above sea
level. It is located
in Cusco, Peru.
It is situated on a mountain ridge above
the Sacred Valley through which the
Urubamba River flows. Most
archaeologists believe that it was built as
an estate for the Inca emperor Pachacuti.
Often mistakenly referred to as the "Lost
City of the Incas", it is the most familiar
icon of Inca civilization. The Incas built
the estate around 1450, but abandoned it
a century later at the time of the
Spanish Conquest.
Use co-occurences
between concepts
and terms to
extract ‘shadow
concepts’
14 This site is a
15th-century Inca
site located 2,430
metres above sea
level. It is located
in Cusco, Peru.
It is situated on a mountain ridge above
the Sacred Valley through which the
Urubamba River flows. Most
archaeologists believe that it was built as
an estate for the Inca emperor Pachacuti.
Often mistakenly referred to as the "Lost
City of the Incas", it is the most familiar
icon of Inca civilization. The Incas built
the estate around 1450, but abandoned it
a century later at the time of the
Spanish Conquest.
Inca
site
Machu
Picchu
Cusco
Inca
empire
Inca
emperor
Peru
Spanish
Conquest
Sacred
Valley
Chankas
Lost
City
Pachacuti
In addition to explicitly used concepts and terms, Machu Picchu is
extracted from the article as a Shadow Concept. As a prerequisite,
one has to provide and analyze a representative text corpus first.
Example:
Use a knowledge
graph together
with
co-occurences
for precise
content
recommendation
15 RavingDe-Void
Scott
attack
Stilinski
friend
shame
O’Brien
woman
married
girl
attractive
Similarepisodes!
love
Find similar episodes from TV Series
Place your screenshot here
16Ontology-driven
Search with
GraphSearch Server
Your knowledge models as key.
Configure semantic search and
graph-based analytics dashboards
over integrated data sets based on
them.
Benefit from Semantic Search
combined with data analytics
facilities.
Place your screenshot here
17Wordsense Induction
Ambiguity controlled: get help from
PoolParty to identify potentially
ambiguous terms.
Benefit from higher precision of your
text mining and entity extraction
service.
Place your screenshot here
18Additional Linked Data
Sources
Link your data to renowned sources
like Getty Vocabularies or PermID
(Thomson Reuters) and extend your
knowledge graph dynamically.
Create seed taxonomies from
DBpedia, with added support for
Russian and Dutch languages.
Semi-automatic
extension of
taxonomies from
Linked Data
19
Enrich your taxonomy with Linked Data
Place your screenshot here
20Graph Visualization
Visualization in PoolParty is fast,
pretty and easy.
Browse your taxonomies and
ontologies based on the great
visualizations we improved for this
release!
CLIMBING THE
SEMANTIC LADDER
From a most efficient Taxonomy Management
towards a Linked Data Platform
21
How PoolParty’s
ontology and
custom schema
management
plays together
with taxonomies
22 Taxonomy
Ontology
Ontology 1
from library
Ontology 2
(imported)
Ontology 3
(custom-made)
Custom Schema
‘Setting the
rules’ for text
mining & entity
extraction via
thesaurus
23
Proper use of an funduscope
requires a bit of practice and
familiarity with the functions of
your device.
Diagnostic Equipment
Ophtalmoscope
Support for
XML to RDF
mapping:
Structured and
unstructured
elements
transformed to
RDF
24 <article>
<title>How to Use an Ophthalmoscope</title>
<metadata>
<id>328832</id>
<author>Mike Miller</author>
<pub_date>March 20, 2016</pub_date>
<version>2</version>
<status>approved</status>
</metadata>
<topics>Ophtalmoscopes</topics>
<text>
Proper use of an funduscope requires a bit of
practice and familiarity with the functions of your
device. Regardless of model type, these hand-held
devices are critical in the evaluation and diagnosis
of a variety of diseases in the eye.
After this examination is complete, follow the
retinal arteries and examine the four vascular
arcades including the superotemporal,
superonasal, inferotemporal, and inferonasal.
</text>
<image>http://my.com/img/99.jpg</image>
</article>
How to Use an
Ophthalmoscope
dct:title
Mike Miller
Michael Miller
skos:prefLabel
skos:altLabel
dct:creator
http://my.com/docs/328832
http://my.com/people/32schema:Article
rdf:type
http://my.com/img/99.jpg
schema:image
skos:subject
Ophtalmoscopes
Funduscopes
Diagnostic
Equipment
skos:prefLabel
skos:subject
skos:altLabel
skos:broader
skos:prefLabel
schema:image
Eye Disease
skos:prefLabel
Semantic
Content
Authoring
FontoXML and
PoolParty
25
TWO
INTEGRATION
SCENARIOS
26
DAM/CMS
Option 1:
Concepts are derived from taxonomy and
tagging is stored together with the asset in
the DAM/CMS
http://apple.com/macmini.jpg
http://apple.com/graph/1234
PoolParty
API
Option 2:
Concepts are derived from taxonomy, and
tagging event is stored in a Linked Data
Store by tying together assets with
concepts from graph.
DAM/CMS
http://apple.com/macmini.jpg
http://apple.com/graph/1234
PoolParty
API
http://apple.com/macmini.jpg
http://apple.com/macmini.jpg
http://apple.com/graph/1234
LD Store
Wed 3 May, 2017User4711
DAM/CMS
API
Pool
Party
Pool
Party
Beyond Semantic
Search
Link unstructured
information to
semantic graphs
with PoolParty
> Learn more
27
PoolParty
GraphSearch
RDF
Graph Database
Unstructured Information (e.g. SharePoint)
Linked Data Warehouse
(Taxonomies and Graphs)
Place your screenshot here
28RDF based ETL
Data processing tasks can be
modelled as pipelines: Make use of
the intuitively usable graphical
interface.
Versatile data integration platform:
Link data from internal and
external data sources in a central
NoSQL linked data warehouse.
Custom plugins: Your data
processing pipelines are highly
customizable by creating your own
data processing units (DPUs).
Place your screenshot here
29Data Analytics
based on Graphs
Make use of Linked Data sources
and Knowledge Graphs to explore
unstructured and structured
information alltogether.
Knowledge Discovery beyond
faceted search becomes true!
GET STARTED
30
Get your test account at
www.poolparty.biz
PoolParty
Academy
Get certified!
31
https://www.poolparty.biz/academy/
Semantic Web
Starter Kit
32
CONNECT
Andreas Blumauer
CEO, Semantic Web Company
▸ andreas.blumauer@semantic-web.com
▸ https://www.linkedin.com/in/andreasblumauer
▸ https://twitter.com/semwebcompany
▸ https://ablvienna.wordpress.com/
33
© Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/

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PoolParty 6.0 - Climbing the Semantic Ladder

  • 1. Andreas Blumauer CEO, Semantic Web Company Helmut Nagy COO, Semantic Web Company POOLPARTY SEMANTIC SUITE FUNCTIONAL OVERVIEW 1 6.0
  • 2. INTRODUCTION 2 Semantic Web Company founder & CEO of Andreas Blumauer developer and vendor of 2004 founded 6.0 current Version active at based on Vienna located part of Taxonomies Knowledge Graphs manages standard for part of is a >200serves customers
  • 3. INTRODUCING SEMANTIC WEB COMPANY Semantic Web Company (SWC) ▸ Founded in 2004 ▸ Based in Vienna ▸ Privately held ▸ 40+ employees, experts in text mining & linked data ▸ ~15-20% revenue growth per year ▸ 2.5 Mio Euro funding for R&D ▸ SWC named to KMWorld’s 2016 and 2017 ‘100 Companies That Matter in Knowledge Management’ ▸ https://www.semantic-web.com 3
  • 4. INTRODUCING POOLPARTY PoolParty Semantic Suite ▸ First release in 2009 ▸ Current version 6.0 ▸ W3C standards compliant ▸ Over 200 installations world-wide ▸ 50% of revenue is reinvested into PoolParty development PoolParty on-premises or used as a cloud service ▸ KMWorld listed PoolParty as Trend-Setting Product 2015 and 2016 ▸ https://www.poolparty.biz/ 4
  • 5. SELECTED CUSTOMER REFERENCES AND PARTNERS SWC head- quarters 5 Customer References ● Credit Suisse ● Boehringer Ingelheim ● Roche ● adidas ● The Pokémon Company ● Canadian Broadcasting Corporation ● Harvard Business School ● Wolters Kluwer ● Talend ● HealthStream ● TC Media ● Techtarget ● Seek ● CafePress ● Pearson - Always Learning ● Education Services Australia ● American Physical Society ● Healthdirect Australia ● World Bank Group ● Inter-American Development Bank ● Renewable Energy Partnership ● Wood MacKenzie ● Oxford University Press ● International Atomic Energy Agency ● Norwegian Directorate of Immigration ● Ministry of Finance (AT) ● Council of the E.U. ● Australian National Data Service Partners ● Accenture ● EPAM Systems ● Enterprise Knowledge ● Mekon Intelligent Content Solutions ● B-S-S Business Software Solutions ● MarkLogic ● Wolters Kluwer ● Digirati ● Quark US East US West AUS/ NZL UK
  • 6. TECHNICAL CORE COMPONENTS 6 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 Extractor & Text Mining Data Integration & Data Linking Unstructured Data Semi- structured Data Structured Data Unified Views PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary Controlled vocabularies as a basis for highly precise entity extraction Entity Extractor informs all incoming data streams about its semantics and links them Schema mapping based on ontologies RDF Graph Database
  • 7. PoolParty as a supervised learning system 7 Content Manager Integrator Taxonomist/ Ontologist Thesaurus Server Extractor PowerTagging uses API is user of is user of is basis of is basis of Index annotates enriches Corpus Learning/ Semantic Analysis CMS extends is basis of analyzes uses API proposes extensions
  • 8. POOLPARTY 6.0 Benefit From New Functions and Features 8
  • 9. Place your screenshot here 9Semantic Middleware Configurator Use PoolParty as the control center for your linked data management! Configure and connect to available indexing engines and graph databases. Set up available linked data sources and visualization tools in one place.
  • 10. Place your screenshot here 10Handling of Large Reference Text Corpora Analyze thousands of documents and extend your knowledge model semi-automatically. Let machines learn from text and benefit from high-quality taxonomies.
  • 11. Corpus analysis results in a network of concepts and terms 11 I need support to continuously extend our taxonomy / controlled vocabulary! skos: Concept Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds skos: Concept skos: Concept Term 1 Term 3 Term 7 Term 8 Term 6 Term 4 Term 2 Term 5 - Relevant terms and phrases - Relevancy of concepts - co-occurence between concepts and terms - co-occurence between terms and terms
  • 12. Place your screenshot here 12Shadow Concept Extraction Benefit from deep semantic analytics of your content. PoolParty now even extracts implicit knowledge from your texts. Make use of corpus learning and statistical language models combined with semantic knowledge graphs.
  • 13. Use co-occurences between concepts and terms to extract ‘shadow concepts’ 13 Inca site Machu Picchu Cusco Inca empire Inca emperor Peru Spanish Conquest Sacred Valley Chankas Lost City Pachacuti Machu Picchu is not mentioned explicitly in the article. But it is mentioned indirectly via its co-occuring terms. By these means, PoolParty can suggest it as a Shadow Concept for annotation. Example: This site is a 15th-century Inca site located 2,430 metres above sea level. It is located in Cusco, Peru. It is situated on a mountain ridge above the Sacred Valley through which the Urubamba River flows. Most archaeologists believe that it was built as an estate for the Inca emperor Pachacuti. Often mistakenly referred to as the "Lost City of the Incas", it is the most familiar icon of Inca civilization. The Incas built the estate around 1450, but abandoned it a century later at the time of the Spanish Conquest.
  • 14. Use co-occurences between concepts and terms to extract ‘shadow concepts’ 14 This site is a 15th-century Inca site located 2,430 metres above sea level. It is located in Cusco, Peru. It is situated on a mountain ridge above the Sacred Valley through which the Urubamba River flows. Most archaeologists believe that it was built as an estate for the Inca emperor Pachacuti. Often mistakenly referred to as the "Lost City of the Incas", it is the most familiar icon of Inca civilization. The Incas built the estate around 1450, but abandoned it a century later at the time of the Spanish Conquest. Inca site Machu Picchu Cusco Inca empire Inca emperor Peru Spanish Conquest Sacred Valley Chankas Lost City Pachacuti In addition to explicitly used concepts and terms, Machu Picchu is extracted from the article as a Shadow Concept. As a prerequisite, one has to provide and analyze a representative text corpus first. Example:
  • 15. Use a knowledge graph together with co-occurences for precise content recommendation 15 RavingDe-Void Scott attack Stilinski friend shame O’Brien woman married girl attractive Similarepisodes! love Find similar episodes from TV Series
  • 16. Place your screenshot here 16Ontology-driven Search with GraphSearch Server Your knowledge models as key. Configure semantic search and graph-based analytics dashboards over integrated data sets based on them. Benefit from Semantic Search combined with data analytics facilities.
  • 17. Place your screenshot here 17Wordsense Induction Ambiguity controlled: get help from PoolParty to identify potentially ambiguous terms. Benefit from higher precision of your text mining and entity extraction service.
  • 18. Place your screenshot here 18Additional Linked Data Sources Link your data to renowned sources like Getty Vocabularies or PermID (Thomson Reuters) and extend your knowledge graph dynamically. Create seed taxonomies from DBpedia, with added support for Russian and Dutch languages.
  • 19. Semi-automatic extension of taxonomies from Linked Data 19 Enrich your taxonomy with Linked Data
  • 20. Place your screenshot here 20Graph Visualization Visualization in PoolParty is fast, pretty and easy. Browse your taxonomies and ontologies based on the great visualizations we improved for this release!
  • 21. CLIMBING THE SEMANTIC LADDER From a most efficient Taxonomy Management towards a Linked Data Platform 21
  • 22. How PoolParty’s ontology and custom schema management plays together with taxonomies 22 Taxonomy Ontology Ontology 1 from library Ontology 2 (imported) Ontology 3 (custom-made) Custom Schema
  • 23. ‘Setting the rules’ for text mining & entity extraction via thesaurus 23 Proper use of an funduscope requires a bit of practice and familiarity with the functions of your device. Diagnostic Equipment Ophtalmoscope
  • 24. Support for XML to RDF mapping: Structured and unstructured elements transformed to RDF 24 <article> <title>How to Use an Ophthalmoscope</title> <metadata> <id>328832</id> <author>Mike Miller</author> <pub_date>March 20, 2016</pub_date> <version>2</version> <status>approved</status> </metadata> <topics>Ophtalmoscopes</topics> <text> Proper use of an funduscope requires a bit of practice and familiarity with the functions of your device. Regardless of model type, these hand-held devices are critical in the evaluation and diagnosis of a variety of diseases in the eye. After this examination is complete, follow the retinal arteries and examine the four vascular arcades including the superotemporal, superonasal, inferotemporal, and inferonasal. </text> <image>http://my.com/img/99.jpg</image> </article> How to Use an Ophthalmoscope dct:title Mike Miller Michael Miller skos:prefLabel skos:altLabel dct:creator http://my.com/docs/328832 http://my.com/people/32schema:Article rdf:type http://my.com/img/99.jpg schema:image skos:subject Ophtalmoscopes Funduscopes Diagnostic Equipment skos:prefLabel skos:subject skos:altLabel skos:broader skos:prefLabel schema:image Eye Disease skos:prefLabel
  • 26. TWO INTEGRATION SCENARIOS 26 DAM/CMS Option 1: Concepts are derived from taxonomy and tagging is stored together with the asset in the DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API Option 2: Concepts are derived from taxonomy, and tagging event is stored in a Linked Data Store by tying together assets with concepts from graph. DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API http://apple.com/macmini.jpg http://apple.com/macmini.jpg http://apple.com/graph/1234 LD Store Wed 3 May, 2017User4711 DAM/CMS API Pool Party Pool Party
  • 27. Beyond Semantic Search Link unstructured information to semantic graphs with PoolParty > Learn more 27 PoolParty GraphSearch RDF Graph Database Unstructured Information (e.g. SharePoint) Linked Data Warehouse (Taxonomies and Graphs)
  • 28. Place your screenshot here 28RDF based ETL Data processing tasks can be modelled as pipelines: Make use of the intuitively usable graphical interface. Versatile data integration platform: Link data from internal and external data sources in a central NoSQL linked data warehouse. Custom plugins: Your data processing pipelines are highly customizable by creating your own data processing units (DPUs).
  • 29. Place your screenshot here 29Data Analytics based on Graphs Make use of Linked Data sources and Knowledge Graphs to explore unstructured and structured information alltogether. Knowledge Discovery beyond faceted search becomes true!
  • 30. GET STARTED 30 Get your test account at www.poolparty.biz
  • 33. CONNECT Andreas Blumauer CEO, Semantic Web Company ▸ andreas.blumauer@semantic-web.com ▸ https://www.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 33 © Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/