1) The document discusses how knowledge representation and ontologies have evolved from closed knowledge bases for specific domains to open knowledge infrastructures that can handle large amounts of diverse data and information at scale. 2) It provides examples of how ontologies and semantic technologies are being used to build intelligent systems that can search, integrate, and automatically process and analyze large datasets. 3) Going forward, ontologies will play an important role in populating knowledge from data and dialog, enabling the automatic exploitation of data by autonomous agents, and enhancing data analytics and mining through semantic representation of datasets, tools, and policies.

1. From Knowledge Bases to Knowledge Infrastructures for
Intelligent Systems
Mathieu d’Aquin
Professor of Informatics, Insight Centre, NUI Galway, Ireland
@mdaquin - mdaquin.net

2. Old-School Knowledge-Based Systems
Cancer
treatment
guidelines
Formalised
Knowledge
Base
Interactive
Decision
Support
Knowledge
encoding and
representation
Reasoning and
interrogation

3. Scale….
Data
Information
Know-
ledge
Machines

4. Scale…
Data
Information
Know-
ledge
Machines
storage
interrogation
processing and
analysis
reasoning and
decision

5. The Web of Data

6. The Web of Data

7. The Web of Data
Gene
Ontology
FMA
OntologyLODE
BIBO
Geo
Ontology
DBPedia
Ontology
Dublin
Core
FOAF
DOAP
SIOC
Music
Ontology
Media
Ontology
rNews

9. Need a different kind of intelligent systems

10. Watson Semantic Web Search Engine
http://watson.kmi.open.ac.uk/

11. Watson Semantic Web Search Engine
Accessing ontologies on the semantic
web through smart APIs - making it
possible to build intelligent systems
using online ontologies as their
knowledge bases.

12. Example application - search

13. Example application - ontology edition

14. Example application - ontology matching

15. Example application - question answering

16. Shameless Plug...

17. Scale…
Data
Information
Know-
ledge
Machines
storage
interrogation
processing and
analysis
reasoning and
decision
Watson: An attempt to scale up
the knowledge level

18. Next step
Data
Information
Know-
ledge
Machines
storage
interrogation
processing and
analysis
reasoning and
decision
Using the knowledge level...
To make large scale
information and data
levels more exploitable

19. Example: The MK Data Hub
Data Infrastructure for the city of Milton
Keynes, enabling sharing and consuming
varied and diverse city scale data.

20. Example applications

21. Example: MK Insight

22. But… a large number of datasets for a large number of applications
MK Data Hub
Analytics
Integration
Curation
Storage
Import
Sensor
Data
Local
Stats
Gov.
Open
Data
...
Mobile
Apps
Dashboards
Business
Intelligence
Social Web
Apps
...

23. Data cataloging needs to do more...
Data cataloging component to index data
based on their provenance, categories,
format, existing use, etc.
But needs to do more to answer questions
such as :
- Can I use those data for a commercial
application? Do I need to attribute
somebody? Even after processing?
- What can this data do? What kind of
things I can apply on it?

24. Ontological approach to data policies
Explicit, semantic representation
of the licences attached to data
As well as the data flows through which
they are processed.

25. Automatic propagation of policies through dataflows

26. Understanding what data can answer
Example of using formal concept
analysis to extract relevant
questions from an RDF (graph)
dataset.

28. Ongoing work on generating interactive interface to ontology-based data
Servicecode
Area
Restaurant
Organisation
isa
population
deprivation
locatedIn
rating
employee
Person
The
population
of Walnut
Tree is 4096
What is the
population
of Walnut
Tree?

29. Towards populating ontologies based on dialog
Mood
Good
Mood
Bad
Mood
Very Bad
Mood
Very
Good
Mood
isa
isa isa
isa
excellent
type
horrible
type
bad
OK
good
typetype
type
better
worse
inverseOf better
TT
better
better
better
Thanks! I don’t know
“great”, is it better or
worse than “OK”? ...
Alexa, tell moody that I’m
feeling great!

30. Towards the automatic exploitation of data
Example, in autonomous agents, using an ontology that provides a typology of
datasets and of data analytics techniques, making them better able to
automatically exploit the data they come across.

31. Conclusion
Knowledge representation and ontology
engineering have gone a long way from top down,
closed, domain centric knowledge-based systems.
From encoding expert knowledge to dealing with
scale, variety and diversity.
Now, becoming central in the necessary automation
of information processing, making data analytics
and mining more directly accessible, with fewer
bottlenecks.

32. Contact: @mdaquin - mdaquin.net