semantic web / linked data technologies

Semantic Web Linked Data

Technologies

Mathieu d’Aquin (@mdaquin) Knowledge Media Institute,

The Open University, UK

Semantic Web Linked Data

Technologies

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.

The Semantic Web

Using the Web to publish, share and exploit information/knowledge

From machines to machines

Using graph-based data modeling, knowledge representation (ontologies) and reasoning

Linked Data

As set of principles and

technologies for a Web of

Data

– Putting the “raw” data

online in a standard

representation (RDF)

– Make the data Web

addressable (URIs)

– Link to other Data

http://lucero-project.info/lb/what-is-linked-data/

http://linkeddata.org

Semantic Web/Linked Data

Technologies?

A stack of technologies and languages – the semantic

web layer cake – more or less from Tim Berners Lee

(W3C, various sources)

Semantic Web/Linked Data

Technologies?

Oh… look another one

Semantic Web/Linked Data

Technologies?

And another…

Semantic Web/Linked Data

Technologies?

And another… (from Benjamin Nowack)

A Stack more like this one:

The Internet

Network protocols to connect machines

The Web

Network of documents connected by

hyperlinks

The Linked Data Web

Graph of data objects connected by

labelled hyperlinks

The Internet

Computer level communication

The Web

Browsing, reading, searching

The Linked Data Web

Data exchange and mashups

Linked Data Open University

Website

Open University

VLE

Mathieu’s

Homepage

Mathieu’s

List of

Publications

Mathieu’s

Twitter

The Web

M366 Course

page

Person: Mathieu

Publication: Pub1

Organisation:

The Open University

Course: M366

Country: Belgium

Book: Mechatronics

author

workFor

availableIn

offers

setBook

The Web of Linked Data

How that works: URIs

Example:

http://data.open.ac.uk/course/aa100

An anchor for linking Let’s say you took this course.

You – took this URI

An identifier for a

data entity Here, the a course offered by

the Open University

An access point to

representation(s) of

the data entity In possibly different

formats…

URI resolving http://data.aalto.fi/id/courses/noppa/dept_T3030

10/09/13 15

In the browser

(Accept: text/html) curl -H "Accept: application/rdf+xml" -L http://data.aalto.fi/id/courses/noppa/dept_T3030

<rdf:Description rdf:about="http://data.aalto.fi/data/id/courses/noppa/dept_T3030"> <rdfs:label>RDF description of Department of Media Technology</rdfs:label> <foaf:primaryTopic> <aiiso:Department rdf:about="http://data.aalto.fi/id/courses/noppa/dept_T3030"> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5077"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2211"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.3101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5006"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5600"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4950"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.1100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6596"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1220"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5701"/> <aiiso:code>T3030</aiiso:code> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4210"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5070"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4400"/> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5310"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5020"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.1110"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6595"/> <foaf:name xml:lang="sv">Institutionen för mediateknik</foaf:name> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1202"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5700"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5600"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.1124"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4900"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.2300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.4101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5200"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2400"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5030"/> <aiiso:part_of> <rdf:Description rdf:about="http://data.aalto.fi/id/courses/noppa/org_SCI"> <aiiso:organization rdf:resource="http://data.aalto.fi/id/courses/noppa/dept_T3030"/> </rdf:Description> </aiiso:part_of> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5700"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4800"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5502"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5350"/>

How that works:

Graph Data modelling (RDF)

http://data.open.ac.uk/course/aa100

“The arts past and present”

http://data.open.ac.uk/saou/ontology#undergraduate

http://purl.org/vocab/aiiso/schema#Module

http://data.open.ac.uk/topic/arts_and_humanities

http://sws.geonames.org/3017382/

“France”

dc:title

rdf:label

rdf:type

dc:subject

courseLevel

geo:lat geo:long

location

How that works:

Querying over HTTP - SPARQL

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

Example:

data.open.ac.uk/query

URI of the query:

http://data.open.ac.uk/query?query=select%20distinct%20...

Applications

Resource

Discovery

Research

Exploration

Social

Simple example

Interactive map of

Open University

Buildings in the UK

Spaces

Floors

ID Address Post-code

Buildings

build1

build1-address

Postcode-mk76aa

name “Berrill building”

data.open.ac.uk

Milton Keynes

inDistrict

Buckinghamshire

inCounty

Mk76aa-location

location

lat long

52.024924 -0.709726

data.ordnancesurvey.co.uk

Another application Location of students showing

particular interest based on their

enrolment into courses

Same thing? Not exactly

ID course post-code

Students

Stays

private

data.open.ac.uk

Topics

data.ordnancesurvey.co.uk

Districts

Location

Clustering

Other resources

DBpedia

Geonames

Analysing own data agains others

Academics in “Arts and Humanities”

most often involved with the media (in

number of news items)

Topics most commonly mentioned by

news outlets own by the BBC (in

number of news items)

From news

clipping data

From dataset about

our researchers From dbpedia.org

ParkJam http://parking.kmi.open.ac.uk/

ParkJam is a mobile

app for Android™ that

gets parking

availability information

from its users, so that

we can all

conveniently find

parking when coming

to work or driving into

town. When you find

some car park is full,

it's real easy to tell

others about it.

Study at the OU mobile app

And more…

OU course

material on

mobile platform

Social connection

through courses

Data

Linked Data

The Semantic Web

The Web

Network of documents connected by

hyperlinks

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

The Semantic Web

Smart, knowledge intensive, connected

systems

The Web

Browsing, reading, searching

The Linked Data Web

Data exchange and mashups

Gene

Ontology

FMA

Ontology LODE

BIBO

Geo

Ontology

DBPedia

Ontology

Dublin

Core

FOAF

DOAP

SIOC

Music

Ontology

Media

Ontology

rNews

Ontologies

Example: Research project in the

history of reading

Experience

Person

Document

Event Location

City Country date: Date

subClassOf

subClassOf

locatedIn

readerInvolved

textInvolved givesBackgroundTo

title: String description: String published: Date

creator/editor

providesExcerptFor

occupation

religion

originCountry

gender

LinkedEvent Ontology

CITO Citation Ontology

Dublin Core

FOAF

DBPedia

Tracking a specific context/topic through

ontology-based querying

Looking at reading,

by military staff

during the first

world war

Example: Generic analytics, taking into account

background knowledge in the domain

Web logs or

application

logs

Web logs or

application

logs

Web logs or

application

logs

Generic

Ontology of

events,

resources

and actions

Domain

specific

extension

ontology (=

background

knowledge)

Analytics

with

domain

specific

filters,

views and

reasoning

Example in learning analytics

Generic ontology

Other use in Personal analytics based on log

integration (see http://uciad.info)

More complex reasoning:

Ontological+epistemic inference on Facebook

• Screenshot

Facebook

graph API

Basic linked

data

Facebook

Ontology

Ontological

inference

(types, relations)

Epistemic

logic theory

of Facebook

Epistemic

inference

(who knows

what)

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

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), include(X,Z),

Ka Post(X)

Data/Information/Knowledge on the Semantic Web

NLP

Information

retrieval

Recommender

Systems

Data Mining

Step further: intelligent applications

and knowledge discovery

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

Simple example:

graph analysis for data integration

Combining Structured and Unstructured Information:

DiscOU (http://discou.info)

data.open.ac.uk

Semantic

Indexing

Semantic Index

Named Entity

Recognition

Podcasts, OpenLearn

Units and Articles

Semantic Entities

(Dbpedia)

Indexes

BBC Programme or iPlayer page

Synopsis

Similarity-

Based Search

Indexes

Interface

Resource

descriptions

Resources URIs +

common topics

Same thing, with just text (discou.info/alfa)

And on course material

PowerAqua: Question Answering

Finding patterns in data:

Data mining

Example:

Using Formal Concept Analysis + Reasoning to build a hierarchy of questions a linked dataset can answer

Use statistical metrics to identify the ones that are most likely to be interesting

Using Linked Data for Interpreting

data patterns

Example: Analysing patient pathways annotated with a french

classification, and exploring the results with ICD-10

Step further: Understanding knowledge

representation and data modeling

The Semantic Web also represents a very large, collaborative base of formally represented knowledge

This can also be mined, to discover things about knowledge representation and data modeling

KMi Watson

Architecture (a Semantic Web Search Engine)

Interface

Watson as a Service

Providing Web

accessible APIs

to a collection of

online

ontologies and

semantic data

sources

PowerAqua: Question Answering

Ontologies on the Semantic Web

Number of entities

Domain covered

Underlying description logic

21 different ontologies with a SeaFood concept

Agreement

Disagreement

http://uciad.info

SeaFood disjointWith Meat

SeaFood subClassOf Meat

Using consensus to assess an ontology

(a new NeOn toolkit plugin

AKT Portal The brighter the blue the higher the positive consensus (higher agreement) The brighter the red the lower the negative consensus (higher disagreement) Dark = controversy: no clear cut between disagreement and agreement

Example: The statements attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles)

Summary Intelligent information

processing

The Semantic Web

Linked Data Web

The Web

Internet

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

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.

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

Thanks to:

ENRICO MOTTA

FOUAD ZABLITH

CARLO ALLOCCA

SALMAN ELAHI

KEERTHI THOMAS

ILARIA TIDDI

ENRICO DAGA

ALESSANDRO ADAMOU