Presentation at the FORTH Institute, Heraklion, 26-05-2014

1. Putting intelligence With examples in
education
Mathieu d’Aquin
(@mdaquin)
Knowledge Media Institute,
The Open University, UK
in web data

2. Mathieu d’Aquin
(@mdaquin)
Knowledge Media Institute,
The Open University, UK
Research Fellow – Background in Artificial Intelligence, Knowledge
Engineering, Reasoning
Working on Semantic Web, Linked Data and Knowledge Technologies
Especially applied to education and personal information
management/Privacy
Research Lab, ~75
people, many industrial
and academic
collaborations, Leader in
semantic web, linked data,
TEL, learning analytics,
new media research
Open and Distance Learning University, the biggest
university in the UK in number of students (~250,000 per
year), 13 regional centres, + national centres. Almost all
teaching at distance.
Putting intelligence With examples in
educationin web data

3. Semantic Web/Linked Data
Technologies?

4. The Semantic Web
Connected knowledge where
entities, concrete and abstract,
have formal attached
meaning/interpretations
The Web
Network of documents
interconnected with hyperlinks
The Linked Data Web
Graph of data objects connected
by labelled hyperlinks

5. Example (in education):
data.open.ac.uk

6. Course information:
600 modules/ description of the course, information about the levels and
number of credits associated with it, topics, and conditions of enrolment.
Research publications and people:
25,000 academic articles / information about authors, dates, abstract and
venue of the publication.
Podcasts:
2220 video podcasts and 1500 audio podcats / short description, topics, link
to a representative image and to a transscript if available, information about
the course the podcast might relate to and license information regarding the
content of the podcast.
Open Educational Resources:
640 OpenLearn Units / short description, topics, tags used to annotate the
resource, its language, the course it might relate to, and the license that
applies to the content.
Youtube videos:
900 videos / short description of the video, tags that were used to annotate
the video, collection it might be part of and link to the related course if
relevant.
University buildings:
100 buildings / address, a picture of the building and the sub-divisions of the
building into floors and spaces.
Library catalogue:
12,000 books/ topics, authors, publisher and ISBN, as well as the course
related.
Others…
Content

7. owl:sameAs
offers
location
data.open.ac.uk/course/m366
sws.geonames.org/2963597 (Ireland)
data.open.ac.uk/organization/the_open_u
niversity
Education.data.gov.uk/id/school/13384

8. SPARQL
Show some basic slides there
Front page
Sparql endpoint
Update follow up application slide
Discou – CRC – tackboard – REF - openlearn
select distinct ?q (count(distinct ?t) as ?n) where {
?q a <http://purl.org/net/mlo/qualification>.
?q <http://data.open.ac.uk/saou/ontology#hasPathway> ?p.
?p <http://data.open.ac.uk/saou/ontology#hasStage> ?s.
{{?s <http://data.open.ac.uk/saou/ontology#includesCompulsoryCourse> ?c}
union
{?s <http://data.open.ac.uk/saou/ontology#includesOptionalCourse> ?c}}.
?c <http://purl.org/dc/terms/subject> ?t.
[] <http://www.w3.org/2004/02/skos/core#hasTopConcept> ?t.
} group by ?q order by desc(?n)
List of courses (degrees, etc.) at The Open University, with
number of topics they cover

9. Simple example
Interactive map
of Open
University
Buildings in the
UK

10. Spaces
Floors
ID Address Post-
code
Buildings
build
1
build1-
address
Postcode-
mk76aa
name “Berrill building”
data.open.ac.uk
Milton
Keynes
inDistrict
Buckingha
mshire
inCounty
Mk76aa-
location
location
lat long
52.024924 -0.709726
data.ordnancesurvey.co.uk

11. Many other (simple) applications

12. Data
Linked Data
The Semantic Web

13. Gene
Ontology
FMA
Ontology
LODE
BIBO
Geo
Ontology
DBPedia
Ontology
Dublin
Core
FOAF
DOAP
SIOC
Music
Ontology
Media
Ontology
rNews
Ontologies

14. Example in education: DiscOU
See discou.info | d'aquin et al @ Demo ISWC 2012

15. Example in research: The
Listening Experience Database
Project with Royal College
of Music and the Open
University's Art Faculty
Goal: Create a large
database of evidence of
people listening to music
(of any genre, at any time,
in any place)
See led.kmi.open.ac.uk

16. Linked data
and
ontologies
to support
data crowd-
sourcing

17. Results
~700 contributions so far

18. Data/Information/Knowledge on the Semantic Web
NLP
Information
retrieval
Recommender
Systems
Data Mining
Step further: intelligent applications
and knowledge discovery

19. The Linked Data Web
Graph of data objects connected
by labelled hyperlinks
The Semantic Web
Connected knowledge where
entities, concrete and abstract,
have formal attached
meaning/interpretations
Intelligent Web information
and knowledge processing
Discovering knowledge models

20. Example in Education: The LinkedUp
Data Catalogue
See data.linkededucation.org/linkedup/catalogue/
See d'Aquin et al in ERCIM News 96

22. Summary visualisation of extracted schema

23. FCA-based
technique to
extract URI
patterns in RDF
datasets

24. Interactive mapping onto canonical ontologies

25. See d'Aquin et al @ WebSci2013
Getting a top
level view of an
area through its
datasets
… and looking at
relationships between
datasets (see the
Datanode ontology)

26. Example(s) in Education:
Learning Analytics
Location of students showing
particular interest based on
their enrolment into courses

27. ID course post-
code
Students
data.open.ac
.uk
Topics
data.ordnancesurvey.
co.uk
Districts
Location
Clustering
Other resources
DBpedia
Geonames

28. How to interpret the results?
See d'Aquin and Jay @ LAK2013
Sequence mining to find
common study pathways and
FCA_Linked Data to interpret
them

31. Can we use linked data
automatically to explain data
patterns?
See Tiddi et al. @ ESWC2014
Taking inspiration from ILP:
Interest in studying Health and Social Care
Positive examples Negative examples
Swansea East London
Machester Milton Keynes
Sheffield Brighton
Southampton Bristol
… ….
Background Knowledge?

32. Linked Data Traversal
See Tiddi et al. @ ESWC2014
Swansea
Manchester
Sheffield
Southampton
East London
Milton Keynes
Bristol
Brighton
51.2 -2.3
Dbpedia:Milton_Keynes
Dbpedia:Labour
241K
yago:unitary_authority
opencyc:unitary_authority
freebase:Bristol
198K
350mm
Dbpedia:Bristol
yago:city
opencyc:city
Dbpedia:Tory
Dbpedia:Southampton
freenase:Southampton
240mm
270K
290K
sameAs
sameAs
sameAssameAs
sameAs
sameAs
type
type
long lat
poppop
pop
pop
pop
party
party
party
rain
rain

33. SummaryIntelligent
information
processing
The Semantic Web
Linked Data Web
The Web
Making smart thing with
what we can find in the web
Naturally integrated
data, flexible model for
rapid development
Large scale,
collaborative,
distributed,
uncontrolled
Connected,
decentralised,
independent

34. Future
Understand this
Make explicit the competence of
data in being used at the upper
level, what is being done to it when
going from raw to processed.
Formalise the practice level in
addition to the symbol, syntax and
semantic levels, to boost
development benefits.
Create generic, standard processes
for the development of intelligence
semantic web systems.

35. Future
And build more
with it...
New environments even
more demanding – more
sophistication and
intelligence required!
See mksmart.org

36. Thank You!
More at:
http://people.kmi.open.ac.uk/mathieu
http://mdaquin.net
m.daquin@open.ac.uk
@mdaquin
These slides at:
http://slideshare.net/mdaquin
Team:
Ilaria Tiddi Alessandro Adamou Enrico Daga Keerthi Thomas

37. More complex reasoning example (in
personal data management): Epistemic
reasoning for privacy on Facebook
• Screenshot
See d'Aquin and Thomas @ Demo ISWC 2013

38. Facebook
graph API
Basic
linked
data
Facebook
Ontology
Ontological
inference
(types,
relations)
Epistemic
logic
theory of
Facebook
Epistemic
inference
(who knows
what)

39. Facebook Ontology (extract)
Person Post
Photo
Video
Status
update
Comment
Agent
App
subclass
author
likes
includes
subclass
author
on
Place
in
{Everyone, Friends_of_Friends, All_Friends, Custom}
scope

40. Example epistemic rules
Ka Post(X) :- author(X, a)
Ka Post(X) :- scope(X, All_Friends),
author(X, Y), friend(Y, a)
Ka Post(X) :- includes(X,Y), friend(Y, a)
Ka wasIn(P, Y) :- includes(X,Y), in(X,P),
Ka Post(X)
Ka wasWith (Y,Z) :- includes(X, Y),
includes(X, Z),
Ka Post(X)