During the conference ConTech 2018, hosted at The Chelsea Harbour Hotel on 29-30 November 2018, Jem Rayfield, Chief Solution Architect at Ontotext gave a presentation on the topic of “Towards data-driven publishing – leveraging knowledge graphs and text analytics to enable new business opportunities”. This presentation was with a focus on how with leveraging AI and cognitive technologies, publishers could create smarter, faster and easier content publishing workflows.

1. Jem Rayfield, November, 2018
Towards Data Driven
Publishing
Leveraging Knowledge Graphs and
Text Analytics
Contech: 2018

2. Outline
● From; Unstructured Ambiguous Content
● Knowledge Graphs
● Ontotext Platform
● To; Data driven publishing

3. How can I get OP?From: Unstructured
Ambiguous Content

7. S
NP VP
Adj N
Stolen painting found by tree
V
PP
P N
S
NP VP
Adj N
Stolen painting found by tree
V
PP
P N

8. Knowledge and Graphs

9. Traditional relational
databases only store
information...

10. Graphs treat the
connections between
information with
equal importance.

11. Knowledge graphs represent
information in a manner
similar to how a human
understands information.

12. Ontotext GraphDb; uses graph
statements to reason and
infer additional knowledge.
Vector space indices
for similarity.

13. Graph; Reasoning & Inference
S = Berners-Lee
P = type
O = Person
S = Person
P = subClassOf
O = Mammal
S = Berners-Lee
P = type
O = Mammal
DATA (RDF)
KNOWLEDGE
(ONTOLOGY)
NEW
Implied
DATA
(RDF)

14. Graph & Vector Space;
Entity Awareness, Similarity
+

15. Big Knowledge Graphs; Provide Awareness
● Important airports near london?
● Most popular banks in UK
● People mentioned together with Apple
in the news

16. Vector Space; Similarity & Concordance
● Find similar content
● Find similar concepts and link
● Find relevant concepts for content

17. Vector
Space
Index
Similarity
Documents
Annotated
With
Graph Ids
GraphDb Vector Space; Similarity & Concordance
urn:Car
urn:Make
urn:Model
urn:Tires
urn:Car
urn:Engine
urn:Tires
urn:SUV
urn:Make
urn:MLModel
urn:Markov
urn:Car
urn:Engine
urn:Tires
urn:SUV
urn:Make
urn:Model
urn:MLModel
urn:Markov
1 1
1
1
0
0
0
0
0
0
1
0
0
1
0
01
1
0
1
0
0
0
0

18. Ontotext Platform

19. Analyses content
Text
Analytics
API
Content
Concept
Suggestions
Classification
Relationships
...
Sentiment
Relationships

20. TA: Vocabulary Aware
Semantic Disambiguation
GraphDB
Vocabulary
Vocabulary Gazetteer
Disambiguation
(ML Model)
NLP Pipeline
Language Detection
POS
...
...
...
Relevance Ranking
(Statistical)
...
Dynamic
Vocabulary
Get
Suggestions
Annotate
Content
Apple : Organisation
Tim Cook : Person, CEO
Tim Cook : Person, Footballer
Samsung : Organisation
Apple : Organisation
Tim Cook : Person, CEO
Tim Cook : Person, Footballer
Samsung : Organisation
87% - Tim Cook : Person, CEO
68% - Apple : Organisation
56% - Samsung : Organisation
Apple CEO Tim Cook
was at a conference
with the CEO of
Samsung. Tim
explained how smart
phones are changing
the consumer
electronics market.
Suggestions
Entity Detection from Vocab
Disambiguation
Relevance

21. Automated (Governed) Machine Learning
Text Analytics
Machine Learnt
Model
Curation
Accept|Reject|Modify
Gold Standard Corpus
[W3C Open Annotation]
Re-train
moderate
suggestmodify
corpus
load
update
model

22. Annotates content with knowledge
Open
Annotation
API
Content
Content
Semantic
Fingerprint

23. Content Vocabulary Annotation Graph
Content
Apple
Organisation
SamsungAnnotation
textpos:123,142
relevance:56%
mentions
Annotation
textpos:123,142
relevance:68%
about
Tim Cook Person
target
target
tag
tag
ceo
type
type
competitor
Annotation
textpos:123,142
relevance:87%
about
target
tag
USA
NASDAQ
Computer
Hardware
location
exchange
sector

24. Understands content
Content
Knowledge Graph
Content
UK
Apple
Samsung
USA
located in
mentions
about
Tim
Cook
USA
NASDAQ
exchange
headquarters
Tim
Cook
Computer
Hardware
ceo
industry
about

25. Understands users
User Data
Knowledge Graph
User
UK
Apple
Inc
Samsung
USA
NASDAQ
lives in
employed by
interested in
Tim
Cook
Computer
Hardware
located in
headquartered in
exchange
ceo
industry

26. Captures behaviour
Event
APIEvents User
Event
Index

27. Understands behaviour
User
Social
Behaviour
User
content:view
content:scroll
content:dwell
concept:follow
hashtag:follow
tweet:view
User
Behaviour

28. Mine social behaviour
User
Behaviour
Social
APIEvents
User
Event
Index

29. Behavioral + Contextual recommendation
Reads
Behavioral
similarity

30. Increased Engagement
Content
Knowledge Graph
User Data
Knowledge Graph
+
Social
Behaviour
User
Behaviour+ =

31. Architecture
Unstructured Content
Users + Events
Content
Concordance
Search
Annotation
User Events
Text Analytics
Recommendation
Knowledge Graph
Semantic
Fingerprint
Structured
Reference data
OP APIs Tools &
Visualisations

32. To; Data driven publishing

33. Dynamic Semantic Publishing
Authoring
● Rapid high value, lower cost content curation
● Capture knowledge and meaning as re-usable data
Search & Discovery
● Unambiguous semantic search
● Recommendation and Similarity
Product
● Re-purpose and aggregate with Business context
● Generate new revenue streams

34. Enhanced Publishing Workflow
Authoring Editorial Production Delivery
Discover
Related
Content
Add
references
Add
Context
Annotate
With Concepts
& Relations
Organise &
Improve
Workflow
Link to
products &
archive
Dynamic
Data driven
products
Content
Transformation
Domain
Modelled
IA
Contextual
Semantic
Search
Recommend
Related
Content
Personalised
Content
Streams

35. DSP - BBC Sport
o Goals
✓ Create a dynamic semantic publishing
platform that assembles web pages
on-the-fly using a variety of data
sources
✓ Deliver highly relevant data to web site
visitors with sub-second response
"The goal is to be able to more easily and accurately aggregate
content, find it and share it across many sources. From these
simple relationships and building blocks you can dynamically build
up incredibly rich sites and navigation on any platform."
John O’Donovan, Chief Technical Architect, BBC

36. The IET
o Goals
✓ Manageable, discoverable, searchable;
Journals, research papers and articles
✓ Semantic search using existing
taxonomies
✓ Intelligent citations and data
provenance
✓ Automated, dynamic repurposing of
content assets
✓ Enable new revenue opportunities

37. Thank you!
Experience the technology with our demonstrators
NOW: Semantic News Portal http://now.ontotext.com
RANK: News popularity ranking for companies http://rank.ontotext.com
FactForge: Knowledge graph of linked open data and news
about People and Organizations
http://factforge.net