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Andreas Blumauer
CEO, Semantic Web Company
Dr Ian Piper
UK Director
PoolParty Semantic Suite
WEBINAR
Feb 15, 2017
Taxonomies and
Ontologies
The Yin and Yang
of Knowledge
Modelling
1
INTRODUCTION
2
Semantic Web
Company
founder &
CEO of
Andreas
Blumauer
developer and
vendor of
2004
founded
5.7
current
Version
active at
based on
Vienna
located
part of
Taxonomies
Knowledge
Graphs
managed
with
standard for part of
is a
>200serves customers
3INTRODUCTION
AGENDA
PART 1 (Andreas Blumauer)
● Introducing the Yin and Yang of Knowledge Modelling
● Semiotic Triangle: implicit and explicit semantics
● Knowledge Acquisition Bottleneck
● Anatomy of a Knowledge Graph
PART 2 (Ian Piper)
● Modelling knowledge with taxonomies and ontologies
● Building content knowledge graphs
4
Types of
Knowledge models
Implicit and explicit Semantics
5
Yin and Yang
of Semantic
Knowledge
Modelling
4 Yin Yang
Passive/female principle in nature Active/male principle in nature
Receive information and classify Define world and reason about it
Taxonomies Ontologies/Rules
Implicit semantics Explicit semantics
Search for ‘taxonomist’ on LinkedIn →
⅔ of found persons are female
Search for ‘ontologist’ on LinkedIn →
⅔ of found persons are male
Better to let them work together!
Implicit
Semantics
▸ Natural languages
▸ Ambiguity versus Universality
▸ Context information and background knowledge needed
7
Susan observes Mike on a
tower with a telescope.
Context is King
▸ Natural languages
▸ Ambiguity versus Universality
▸ Context information and background knowledge needed
8
- Susan and Mike are persons.
- Yesterday Michael bought a Celestron.
- If one buys something, (s)he owns it and
can use it.
- Mike and Michael is the same person.
- A Celestron is a telescope.
Susan observes Mike on a
tower with a telescope.
Semiotic Triangle
The level of
efficiency of an
Interpretant
depends mainly on
its ability to
correctly link a
symbol with the
object it stands for.
9
Telescope
Symbol
Object
Interpretant
Semiotic Triangle
The level of
efficiency of an
Interpretant
depends mainly on
its ability to
correctly link a
symbol with the
object it stands for.
10
Telescope
Symbol
Object
Interpretant
http://dbpedia.org/
resource/Telescope
How can various
Knowledge
Modellers build
together Strong
Artificial
Intelligence?
11 Natural languages
Taxonomies
Schemas/Ontologies
Statisticalmodels
Computational Linguists
Taxonomists
DataScientists
Ontologists
Knowledge Acquisition
Bottleneck
Computer (networks)
need to be programmed
with sufficient amount of
knowledge before it can
begin to learn
semi-automatically
12
Knowledge Domain
Knowledge
Modellers
Knowledge Model
semantic gap
Domain
Experts
How does nature
go around similar
learning
bottlenecks?
13 Bla bla
bla bla.
Bla bla
bla bla
The stove is on.
The stove is hot!
Ontological model → reasoningTaxonomical model → is-a abstractions
Bla stove
bla bla.
Bla bla
bla hot
Switched on
devices are
dangerous
devices.
Switched on devices are
dangerous, only if the
operating temperature
is above 100 degrees
and the automatic
shutdown mechanism is
broken.
The stove is on.
The stove is hot!
Statistical model/cooccurences → is related
The stove is on.
The stove is hot!
Bla bla bla bla
Bla bla bla bla.
Co-occurence
model
14
Reference
Corpus
- Websites
- PDF, Word, …
- Abstracts from
DBpedia
- RSS Feeds
Term 8
Term 3
Term 7
Term 8
Term 6
Term 9
Term 5
Term 10
- Relevant terms and phrases
- Relevancy of terms
- co-occurence between terms and terms
Term 1
Term 4
Term 2
Introducing
some explicit
semantics
▸ Taxonomies
▸ SKOS taxonomies are concept and resource-based knowledge models
15
skos:
Concept
Celestron
skos:prefLabel
skos:
Concept
skos:related
Mike
skos:prefLabel
Michael
skos:altLabel
skos:
Concept
Susan
skos:prefLabel
skos:related skos:
Concept
Scheme
skos:inScheme
skos:inScheme
Person
skos:prefLabel
skos:
Concept
Tower of Babel
skos:prefLabel
skos:
Concept
skos:broader
Telescope
skos:prefLabel
skos:related
Corpus analysis
results in a
network of
concepts and
terms
16
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
PoolParty
The Combination
of Machine
Learning & Human
Intelligence
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
17
Use co-occurences
between concepts
and terms to extract
‘shadow concepts’
18 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:
From taxonomies
to ontologies
19
my:
concepts#1
Susan
skos:prefLabel
skos:
Conceptrdf:type
‘2017-02-15’
dct:modified
my:
persons#1
dc:creator
foaf:
Person
rdf:type
Alex
foaf:name
Ontologies:
Some more
explicit
semantics
▸ Ontologies
▸ Ontologies classify things and define more specific relations and attributes
▸ Locally and globally recognised ontologies can be combined
▸ Ontologies can have various levels of expressivity (RDFS, OWL)20
schema:
Product
Telescope
schema:name
foaf:
Person
schema:owns
Mike
foaf:nick
Michael
foaf:givenName
foaf:
Person
Susan
foaf:givenName
myOnt:observes
geo:
Spatial
Thing
Tower of Babel
skos:prefLabel
schema:
Brand
schema:brand
Celestron
schema:name
myOnt:visits
Reasoning
21 If someone buys a Celestron,
(s)he can use it as a telescope.
buys
uses
is ais subproperty of
Reasoning over
SKOS taxonomies
using OWL
22
Celestron
Telescope
Optical
device
NEXSTAR SLT
Take your
explorations to
new heights with
Celestron's
NexStarSLT.
Available with a variety of optical tubes up
to 127 mm in aperture, the NexStar SLT has
something for everyone. Beginners will
appreciate the intuive SkyAlign technology,
which makes aligning your device's
computer to the night sky as easy as
centering three bright objects in the
eyepiece. The NexStar SLT is a precision
instrument that can grow with you in the
hobby of amateur astronomy for years to
come.
I’m looking for
documents about
Optical Devices
skos:broader
skos:broader is a owl:TransitiveProperty
Reasoning over
SKOS taxonomies
using SPARQL 1.1
property paths
More performant!
See also: SHACL
23
Celestron
Telescope
Optical
device
NEXSTAR SLT
Take your
explorations to
new heights with
Celestron's
NexStarSLT.
Available with a variety of optical tubes up
to 127 mm in aperture, the NexStar SLT has
something for everyone. Beginners will
appreciate the intuive SkyAlign technology,
which makes aligning your device's
computer to the night sky as easy as
centering three bright objects in the
eyepiece. The NexStar SLT is a precision
instrument that can grow with you in the
hobby of amateur astronomy for years to
come.
I’m looking for
documents about
Optical Devices
skos:broader
…. WHERE ?s skos:broader+ ?o …..
Combine
SKOS-XL with
ontologies
▸ Use custom relations
between SKOS-XL labels24
skos-xl:
Label
Switzerland@en
skos-xl:
Label
Swiss
Confederation@en
skos-xl:altLabel
my:isPredecessor
geo:
Spatial
Thing
skos-xl:prefLabel
Building
Knowledge Graphs
Anatomy of an
Enterprise Knowledge Graph
25
Instance data
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
Schema data
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
image
CC BY-SA 3.0
Metadata
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
CC BY-SA 3.0
Taxonomies and Thesauri
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
prefLabel
Piazza
altLabel
Town Square
broader
related
related
opening
Hours
CC BY-SA 3.0
Links between internal and external data
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
prefLabel
Piazza
altLabel
Town Square
broader
related
related
same as
opening
Hours
The Peggy
Guggenheim
Collection is
a modern art museum
on the Grand Canal in
the Dorsoduro
sestiere of Venice,
Italy.
same as
CC BY-SA 3.0
Mappings to data and documents
stored in other systems
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
prefLabel
Piazza
altLabel
Town Square
broader
related
related
opening
Hours
Modelling knowledge
Taxonomies, ontologies, linked data and structured data
32
Linkedin
article
33
http://preview.tinyurl.com/z66vp5s
A taxonomy
34
An ontology
35
Geography as
taxonomy
36
Plant
taxonomy and
ontology
37
Modelling
plants in a
taxonomy
38
A taxonomy in
PoolParty
39
Ontology of
plant ranks
40
PoolParty’s
ontology and
custom
schema
management
41 Taxonomy
Ontology
Ontology 1
from library
Ontology 2
(imported)
Ontology 3
(custom-made)
Custom Schema
A custom
ontology in
PoolParty
42
43
44
Yin and yang
45
Modelling knowledge in PoolParty
gives the best of both worlds:
● Usability of taxonomy design
● Flexibility of ontology design
Content knowledge graphs
46
How taxonomies can work with structured content
Simple content
object
structure
47
Content model
as a graph
48
Simplified
DITA model
49
Building a
content
knowledge
graph - step 1
50
Building a
content
knowledge
graph - step 2
51
52
53
54
55
Content
knowledge
graphs:
summary
56
A content knowledge graph
approach:
● Allows separation of concerns and reduces
dependencies
● Is a major step in development of an
enterprise knowledge graph
● Provides an incremental route from current
state
● Illustrates the benefits of the Yin and Yang of
taxonomies and ontologies
Meet the
PoolParty Team at
some major events
in 2017
57
June 12-14, London
MarkLogic World 2017 EMEA
> More information
Sep 11-14, Amsterdam
13th Int. Conference on Semantic Systems
> More information
Nov 6-9, Washington D.C.
KM World and
Taxonomy Bootcamp
> More information
Oct 17-18, London
Taxonomy Bootcamp
> More information
Oct 21-25, Vienna
16th Int. Semantic Web Conference
> More information
PoolParty
Academy
Get certified!
58
https://www.poolparty.biz/academy/
CONNECT
Andreas Blumauer
CEO, Semantic Web Company
▸ a.blumauer@semantic-web.at
▸ http://at.linkedin.com/in/andreasblumauer
▸ https://twitter.com/semwebcompany
▸ https://ablvienna.wordpress.com/
59
© Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/
CONNECT
Dr Ian Piper
UK PoolParty Director
Tellura Information Services Ltd.
▸ i.piper@semantic-web.at
▸ https://www.linkedin.com/in/ianpiper
▸ https://twitter.com/tellura_tweets
▸ http://tellura.co.uk/
60
© Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/

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Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling

  • 1. Andreas Blumauer CEO, Semantic Web Company Dr Ian Piper UK Director PoolParty Semantic Suite WEBINAR Feb 15, 2017 Taxonomies and Ontologies The Yin and Yang of Knowledge Modelling 1
  • 2. INTRODUCTION 2 Semantic Web Company founder & CEO of Andreas Blumauer developer and vendor of 2004 founded 5.7 current Version active at based on Vienna located part of Taxonomies Knowledge Graphs managed with standard for part of is a >200serves customers
  • 4. AGENDA PART 1 (Andreas Blumauer) ● Introducing the Yin and Yang of Knowledge Modelling ● Semiotic Triangle: implicit and explicit semantics ● Knowledge Acquisition Bottleneck ● Anatomy of a Knowledge Graph PART 2 (Ian Piper) ● Modelling knowledge with taxonomies and ontologies ● Building content knowledge graphs 4
  • 5. Types of Knowledge models Implicit and explicit Semantics 5
  • 6. Yin and Yang of Semantic Knowledge Modelling 4 Yin Yang Passive/female principle in nature Active/male principle in nature Receive information and classify Define world and reason about it Taxonomies Ontologies/Rules Implicit semantics Explicit semantics Search for ‘taxonomist’ on LinkedIn → ⅔ of found persons are female Search for ‘ontologist’ on LinkedIn → ⅔ of found persons are male Better to let them work together!
  • 7. Implicit Semantics ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 7 Susan observes Mike on a tower with a telescope.
  • 8. Context is King ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 8 - Susan and Mike are persons. - Yesterday Michael bought a Celestron. - If one buys something, (s)he owns it and can use it. - Mike and Michael is the same person. - A Celestron is a telescope. Susan observes Mike on a tower with a telescope.
  • 9. Semiotic Triangle The level of efficiency of an Interpretant depends mainly on its ability to correctly link a symbol with the object it stands for. 9 Telescope Symbol Object Interpretant
  • 10. Semiotic Triangle The level of efficiency of an Interpretant depends mainly on its ability to correctly link a symbol with the object it stands for. 10 Telescope Symbol Object Interpretant http://dbpedia.org/ resource/Telescope
  • 11. How can various Knowledge Modellers build together Strong Artificial Intelligence? 11 Natural languages Taxonomies Schemas/Ontologies Statisticalmodels Computational Linguists Taxonomists DataScientists Ontologists
  • 12. Knowledge Acquisition Bottleneck Computer (networks) need to be programmed with sufficient amount of knowledge before it can begin to learn semi-automatically 12 Knowledge Domain Knowledge Modellers Knowledge Model semantic gap Domain Experts
  • 13. How does nature go around similar learning bottlenecks? 13 Bla bla bla bla. Bla bla bla bla The stove is on. The stove is hot! Ontological model → reasoningTaxonomical model → is-a abstractions Bla stove bla bla. Bla bla bla hot Switched on devices are dangerous devices. Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken. The stove is on. The stove is hot! Statistical model/cooccurences → is related The stove is on. The stove is hot! Bla bla bla bla Bla bla bla bla.
  • 14. Co-occurence model 14 Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds Term 8 Term 3 Term 7 Term 8 Term 6 Term 9 Term 5 Term 10 - Relevant terms and phrases - Relevancy of terms - co-occurence between terms and terms Term 1 Term 4 Term 2
  • 15. Introducing some explicit semantics ▸ Taxonomies ▸ SKOS taxonomies are concept and resource-based knowledge models 15 skos: Concept Celestron skos:prefLabel skos: Concept skos:related Mike skos:prefLabel Michael skos:altLabel skos: Concept Susan skos:prefLabel skos:related skos: Concept Scheme skos:inScheme skos:inScheme Person skos:prefLabel skos: Concept Tower of Babel skos:prefLabel skos: Concept skos:broader Telescope skos:prefLabel skos:related
  • 16. Corpus analysis results in a network of concepts and terms 16 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
  • 17. PoolParty The Combination of Machine Learning & Human Intelligence 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 17
  • 18. Use co-occurences between concepts and terms to extract ‘shadow concepts’ 18 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:
  • 20. Ontologies: Some more explicit semantics ▸ Ontologies ▸ Ontologies classify things and define more specific relations and attributes ▸ Locally and globally recognised ontologies can be combined ▸ Ontologies can have various levels of expressivity (RDFS, OWL)20 schema: Product Telescope schema:name foaf: Person schema:owns Mike foaf:nick Michael foaf:givenName foaf: Person Susan foaf:givenName myOnt:observes geo: Spatial Thing Tower of Babel skos:prefLabel schema: Brand schema:brand Celestron schema:name myOnt:visits
  • 21. Reasoning 21 If someone buys a Celestron, (s)he can use it as a telescope. buys uses is ais subproperty of
  • 22. Reasoning over SKOS taxonomies using OWL 22 Celestron Telescope Optical device NEXSTAR SLT Take your explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader skos:broader is a owl:TransitiveProperty
  • 23. Reasoning over SKOS taxonomies using SPARQL 1.1 property paths More performant! See also: SHACL 23 Celestron Telescope Optical device NEXSTAR SLT Take your explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader …. WHERE ?s skos:broader+ ?o …..
  • 24. Combine SKOS-XL with ontologies ▸ Use custom relations between SKOS-XL labels24 skos-xl: Label Switzerland@en skos-xl: Label Swiss Confederation@en skos-xl:altLabel my:isPredecessor geo: Spatial Thing skos-xl:prefLabel
  • 25. Building Knowledge Graphs Anatomy of an Enterprise Knowledge Graph 25
  • 26. Instance data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum
  • 27. Schema data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image
  • 28. CC BY-SA 3.0 Metadata prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours
  • 29. CC BY-SA 3.0 Taxonomies and Thesauri prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related opening Hours
  • 30. CC BY-SA 3.0 Links between internal and external data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related same as opening Hours
  • 31. The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. same as CC BY-SA 3.0 Mappings to data and documents stored in other systems prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related opening Hours
  • 32. Modelling knowledge Taxonomies, ontologies, linked data and structured data 32
  • 41. PoolParty’s ontology and custom schema management 41 Taxonomy Ontology Ontology 1 from library Ontology 2 (imported) Ontology 3 (custom-made) Custom Schema
  • 43. 43
  • 44. 44
  • 45. Yin and yang 45 Modelling knowledge in PoolParty gives the best of both worlds: ● Usability of taxonomy design ● Flexibility of ontology design
  • 46. Content knowledge graphs 46 How taxonomies can work with structured content
  • 48. Content model as a graph 48
  • 52. 52
  • 53. 53
  • 54. 54
  • 55. 55
  • 56. Content knowledge graphs: summary 56 A content knowledge graph approach: ● Allows separation of concerns and reduces dependencies ● Is a major step in development of an enterprise knowledge graph ● Provides an incremental route from current state ● Illustrates the benefits of the Yin and Yang of taxonomies and ontologies
  • 57. Meet the PoolParty Team at some major events in 2017 57 June 12-14, London MarkLogic World 2017 EMEA > More information Sep 11-14, Amsterdam 13th Int. Conference on Semantic Systems > More information Nov 6-9, Washington D.C. KM World and Taxonomy Bootcamp > More information Oct 17-18, London Taxonomy Bootcamp > More information Oct 21-25, Vienna 16th Int. Semantic Web Conference > More information
  • 59. CONNECT Andreas Blumauer CEO, Semantic Web Company ▸ a.blumauer@semantic-web.at ▸ http://at.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 59 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/
  • 60. CONNECT Dr Ian Piper UK PoolParty Director Tellura Information Services Ltd. ▸ i.piper@semantic-web.at ▸ https://www.linkedin.com/in/ianpiper ▸ https://twitter.com/tellura_tweets ▸ http://tellura.co.uk/ 60 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/