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

▸ a.blumauer@semantic-web.at▸ http://at.linkedin.com/in/andreasblumauer▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/

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© Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/

CONNECT

Dr Ian PiperUK PoolParty DirectorTellura Information Services Ltd.▸ i.piper@semantic-web.at▸ https://www.linkedin.com/in/ianpiper ▸ https://twitter.com/tellura_tweets ▸ http://tellura.co.uk/

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© Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/