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
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Andreas BlumauerCEO, Semantic Web Company
Dr Ian PiperUK Director
PoolParty Semantic Suite
WEBINARFeb 15, 2017
Taxonomies and Ontologies
The Yin and Yang of Knowledge Modelling
1
INTRODUCTION
2Semantic Web
Company
founder & CEO of
Andreas Blumauer
developer and vendor of
2004founded
5.7
current Version
active at
based on
Vienna
located
part ofTaxonomies Knowledge
Graphs
managed with
standard for part of
is a
>200serves customers
3INTRODUCTION
AGENDAPART 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
7Susan 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
TelescopeSymbol
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
TelescopeSymbol
Object
Interpretant
http://dbpedia.org/resource/Telescope
How can various Knowledge Modellers build together Strong Artificial Intelligence?
11 Natural languages
Taxonomies
Schemas/O
ntologiesStat
istic
al m
odel
s
Computational Linguists
Taxonomists
Data
Sci
entis
ts 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
14ReferenceCorpus
- 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 models15
skos:Concept
Celestron
skos:prefLabel
skos:Concept
skos:related
Mike
skos:prefLabel
Michaelskos:altLabel
skos:Concept
Susan
skos:prefLabel
skos:related skos:ConceptScheme
skos:inScheme
skos:inScheme
Person
skos:prefLabel
skos:Concept
Tower of Babel
skos:prefLabel
skos:Concept
skos:broader
Telescope
skos:prefLabelskos:related
Corpus analysis results in a network of concepts and terms
16 I need support to continuously extend our taxonomy / controlled vocabulary!
skos:Concept
ReferenceCorpus
- 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
ThesaurusServer
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
analyzesuses 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
CuscoInca
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
19my:
concepts#1
Susan
skos:prefLabel
skos:Conceptrdf:type
‘2017-02-15’
dct:modified
my:persons#1
dc:creatorfoaf:
Personrdf:type
Alexfoaf: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
Michaelfoaf:givenName
foaf:Person
Susan
foaf:givenName
myOnt:observesgeo:
SpatialThing
Tower of Babel
skos:prefLabel
schema:Brand
schema:brand
Celestron
schema:namemyOnt: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 1.1property 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:SpatialThing
skos-xl:prefLabel
Building Knowledge Graphs
Anatomy of an Enterprise Knowledge Graph
25
Instance data
prefLabel
VeniceprefLabel
St. Mark’s Square
altLabel
Piazza San Marco
Peggy Guggenheim
Museum
Schema data
prefLabel
VeniceprefLabel
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
openingHours
image
CC BY-SA 3.0
Metadata
prefLabel
VeniceprefLabel
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
openingHours
CC BY-SA 3.0
Taxonomies and Thesauri
prefLabel
VeniceprefLabel
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
altLabelTown Square
broader
related
related
openingHours
CC BY-SA 3.0
Links between internal and external data
prefLabel
VeniceprefLabel
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
altLabelTown Square
broader
related
related
same as
openingHours
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 documentsstored in other systems
prefLabel
VeniceprefLabel
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
altLabelTown Square
broader
related
related
openingHours
Modelling knowledgeTaxonomies, 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 1from 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, LondonMarkLogic World 2017 EMEA> More information
Sep 11-14, Amsterdam13th Int. Conference on Semantic Systems> More information
Nov 6-9, Washington D.C.KM World and Taxonomy Bootcamp> More information
Oct 17-18, LondonTaxonomy Bootcamp> More information
Oct 21-25, Vienna16th Int. Semantic Web Conference> More information
PoolParty Academy
Get certified!
58
https://www.poolparty.biz/academy/
CONNECT
Andreas BlumauerCEO, Semantic Web Company
▸ [email protected]▸ 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 PiperUK PoolParty DirectorTellura Information Services Ltd.▸ [email protected]▸ 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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