Wm unit1.6-slides-semantic web-final

Universidad Autónoma de Madrid
Escuela Politécnica Superior
Máster Universitario en Investigación e Innovación en
Tecnologías de la Información y las Comunicaciones

Web Mining

Mining semi-structured data

The Semantic Web: From words to meanings

Miriam Fernández, m.fernandez@open.ac.uk
Knowledge Media Institute, The Open University, UK

1

•  1. Introduction
•  Unstructured data vs. structured data
•  Data vs. information vs. knowledge
•  Motivation
•  2. The Semantic Web
•  What is the Semantic Web?
•  The Semantic Web weapons: ontologies & representation languages
•  The Semantic Web tools: ontology editors & triple stores

The Semantic Web: From words to meanings Web Mining
Máster Universitario en Investigación e Innovación en Tecnologías de la Información y las Comunicaciones
Escuela Politécnica Superior, Universidad Autónoma de Madrid

2

•  3. Two paths towards the Sematic Web vision
•  Metadata embedded in HTML
•  Linked Data
-‐  An example
•  4. Sematic Web applications
•  Search engines
•  Recommendations
•  Open government
•  Institutions
-‐  The Open University
-‐  BBC


3

•  Motivation
•  The Semantic Web weapons: ontologies & representation languages


What is … ? 4


What is … ? 5

Rigid structure: well deﬁned
vocabularies and representation
(e.g. databases)

Flexible structure

(e.g. tags, taxonomies)

No structure

(e.g. free text)


What is … ? 6

Iván in Italy!

Semantic interpretation

A picture!

Syntactic interpretation


Why do we need to add structure? THE CHALLENGE 7

Is this a person, an object, a
place? I don’t understand
anything! L

Semantic interpretation

A picture!

Syntactic interpretation

Find the way machines can extract & interpret knowledge!

Why is this important? 8

We have entered the Zettabyte era! (a billion terabytes)
http://www.emc.com/collateral/demos/microsites/emc-digital-universe-2011/index.htm



•  Information overwhelming
•  We need mechanisms to support
-‐  better information search
-‐  better information integration
-‐  automatic knowledge extraction

•  User generated content is generally unstructured
•  Machines can not understand such content!



Many pages later I still didn’t
find what I was looking for!
L

The search engine didn’t
understood my query



Available houses
Moving to England… Crime reports in
looking for a place to live different areas

Amenities

Far too much distributed and unrelated information

12

•  Why is this important?
•  The Semantic Web weapons: ontologies & representation
languages


The Semantic Web 13

“I have a dream for the Web in which computers become capable of
analysing all the data on the Web”

"The Semantic Web is an extension of the current Web in which
information is given well-defined meaning, better enabling computers
and people to work in cooperation."
Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American,
May 2001

“The Semantic Web is a vision: the idea of having data on the web
defined and linked in a way that it can be used by machines not just for
display purposes, but for automation, integration and reuse of data
across various applications”
http://www.w3.org/2001/sw


The Semantic Web 14

To the Web of machine understandable content
The Web of objects and relations
Structured

Unstructured

From the Web of human understandable content
The Web of documents and links


The Semantic Web weapons: ONTOLOGIES 15

An ontology is a formal, explicit
specification of a shared conceptualization
Formal: machine-readable
Explicit: concepts, properties,
relations, functions, constraints,
axioms are explicitly defined
Shared: consensual knowledge
Conceptualization: abstract
model and simplified vide of
some phenomenon in the world
that we want to represent

The Semantic Web weapons: ONTOLOGIES 16

How to model the world of Pizzas?
ontology knowledge base
Classes Properties Individuals

Cuatrro

Formaggi

hasCountryOrigin

Italy

Restrictions

h>p://owl.cs.manchester.ac.uk/tutorials/protegeowltutorial/resources/ProtegeOWLTutorialP4_v1_3.pdf


The Semantic Web weapons: 17

REPRESENTATION AND QUERY LANGUAGES

Querying data (SPARQL)

Capturing ontologies (OWL)

Expressing and linking data,
including metadata (RDF)

Assigning unambiguous names (URI)


RDF (Resource Description Framework) 18

•  RDF identifies things using Web identifiers (URIs), and describes
resources with properties and property values.
•  The triple representation (subject, predicate, object)
<?xml
version="1.0"?>

<rdf:RDF

xmlns:rdf="h>p://www.w3.org/1999/02/22-‐rdf-‐syntax-‐ns#"

Xmlns:epsp=h>p://www.ir.ii.uam.es/personnel#>

<rdf:DescripYon

rdf:about="h>p://www.ir.ii.uam.es/personnel/IvanCantador">

<epsp:name>Ivan</epsp:name>

<epsp:lastname>Cantador</epsp:lastname>

<epsp:naYonality>Spanish</epsp:naYonality>

<rdf:type>Assistant
Professor</rdf:type>

</rdf:DescripYon>

</rdf:RDF>

h>p://www.ir.ii.uam.es/personnel/name

h>p://www.ir.ii.uam.es/

Ivan

personnel/IvanCantador


RDFS (RDF Schema) 19

•  RDFS provides the framework to describe classes and properties. It
allows the creation of hierarchies
<?xml
version="1.0"?>

<rdf:RDF


xmlns:rdfs="h>p://www.w3.org/2000/01/rdf-‐schema#"

xml:base=”
h>p://www.eps.uam.es#

<rdfs:Class
rdf:ID=”Professor"
/>

<rdfs:Class
rdf:ID=”Assistant
Professor">

<rdfs:subClassOf
rdf:resource=”#Professor"/>

</rdfs:Class>

<rdf:Property
rdf:ID=“teachesSubject”>

<rdfs:Domain
rdf:resource=“#Professor"/>

<rdfs:Range
rdf:resource=“#Subject"/>

</rdf:Property>

</rdf:RDF>


OWL (Web Ontology Language) 20

•  OWL is a stronger language with greater machine interpretability than
RDF/RDFS (reasoning support)
•  OWL Little / OWL DL / OWL Full Value
constraints:

owl:allValuesFrom

<?xml
version="1.0"?>

owl:someValuesFrom

<rdf:RDF

owl:hasValue


xmlns:rdfs=h>p://www.w3.org/2000/01/rdf-‐schema#

Cardinality
constraints:

xmlns:owl
=
"h>p://www.w3.org/2002/07/owl#"

owl:cardinality

xml:base=“h>p://www.eps.uam.es#

owl:minCardinality

owl:maxCardinality

<owl:Class
rdf:ID=”Professor”>

….

<owl:restricYon>

<owl:onProperty
rdf:resource=“#hasAcademicTitle"/>

<owl:hasValue>
PhD^^h>p://www.w3.org/2001/XMLSchema#string</owl:hasValue>

<owl:restricYon>

</owl:Class>

</rdf:RDF>
Ivan
has
a
PhD
-‐>
therefore

Ivan
can
be
professor!


SPARQL (SPARQL Protocol and RDF Query Language) 21

•  SPARQL is a query language for RDF
•  Based on the triple representation (subject, predicate, object)
•  SPARQL 1.1 is W3C Recommendation since 21st March 2013
PREFIX
epsp:
<h>p://www.eps.uam.es>

<rdf:RDF


SELECT
?x
?name

WHERE

{

?x
rdf:type
epsp:Assistant
Professor.

?x
epsp:name
?name.
Can
you
Google
this?

?x
epsp:naYonality
“Spanish”}

SPARQL
allows
the
construcYon
of
very
powerful
queries

-‐
Filtering
elements

-‐
Querying
named
graphs

-‐
Ordering
by/
disYnct
/
reduced
/
oﬀset
/
limit

-‐
Count
/
sum
/
avg
/
min
/
max
/
GroupConcat


The Semantic Web tools: ONTOLOGY EDITORS 22

•  An ontology editor lets the creation or manipulation of ontologies

h>p://en.wikipedia.org/wiki/Ontology_editor


The Semantic Web tools: TRIPLE STORES 23

•  A triple store lets persistent storage of RDF data and querying via
SPARQL

h>p://www.garshol.priv.no/blog/231.html


24

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Two paths towards the SW vision: ADDING DATA TO THE WEB
25

•  Microformats
•  RDFa
•  Schema.org
•  HTML5

•  Linked Data
•  Publish the data online in a standard, web enabled representation
(RDF)
•  Make the data web addressable (URI)
•  Link with other data


26

•  Linked Data
-‐  An example
•  Social Networks
•  Institutions
-‐  BBC


Microformats 27

•  Microformats use existing HTML attributes to embed structured data
types in an HTML document
•  Expressive power is limited as microformats are only designed to pre-
defined vocabularies
•  No interlinking between entities
<div
class="vcard">

<a
class="fn
org
url"
href="h>p://www.eps.uam.es/">EPS</a>

<div
class="adr">

<span
class="street-‐address">Francisco
Tomas
y
Valiente</span
>

<span
class="locality">Madrid</span>,

<span
class="postal-‐code">28049</span>

<span
class="country-‐name">Spain</span
>

</div>

</div>

h>p://microformats.org


RDFa (Resource Description Framework in Attributes) 28

•  W3C standard for embedding RDF data in HTML documents
•  A set of new HTML attributes ands specs of how to use them
•  RDFa is just a syntax, the publisher has to choose the vocabulary
•  RDFa is domain independent
<html>

<body
vocab="h>p://xmlns.com/foaf/0.1/">

...

<h2
property=”name”>Ivan
Cantador</h2>

<p><span
property=”gender">undeﬁned</span></p>

...

</body>

</html>

Web
standard
since
June
2012

h>p://www.w3.org/TR/rdfa-‐core/

h>p://www.w3.org/TR/xhtml-‐rdfa-‐primer


RDFa (Resource Description Framework in Attributes) 29

•  Used by Facebook! … among others ;)
•  RDF vocabulary to be used in conjunction with RDFa

<html
xmlns:og="h>p://opengraphprotocol.org/schema/">

<head>

<Ytle>The
Rock
(1996)</Ytle>

<meta
property="og:Ytle"
content="The
Rock"
/>

<meta
property="og:type"
content="movie"
/>

<meta
property="og:url"
content="h>p://www.imdb.com/Ytle/>0117500/"
/>

<meta
property="og:image"
content="h>p://ia.media-‐imdb.com/images/
rock.jpg"
/>
…

</head>
...

</html>


HTML5 (microdata) 30

•  HTML introduces inline elements (text-level semantics) to describe
specific type of information
<div
itemscope
itemtype="h>p://data-‐vocabulary.org/Person">

My
name
is
<span
itemprop="name">Ivan</span>

Here
is
my
home
page:

<a
href="h>p://arantxa.ii.uam.es/~cantador/"

itemprop="url">h>p://arantxa.ii.uam.es/~cantador/</a>

I
live
in
Madrid,
and
work
as
an
<span
itemprop="Ytle">Assistant

Professor</span>

at
<span
itemprop="aﬃliaYon">UAM</span>.

</div>

•  Itemid

•  Itemprop

•  Itemref

•  Itemscope

•  itemtype

h>p://dev.w3.org/html5/md-‐LC/#encoding-‐microdata


Schema.org 31

•  Provides a collection of vocabularies, a long with the microdata
format, that are recognised by the major search engine providers (de
facto standard)
<div
itemscope
itemtype=h>p://schema.org/Movie>

<h1
itemprop="name">Pirates
of
the
Carribean:
On
Stranger
Tides
(2011)</h1>

Director:

<div
itemprop="director"
itemscope
itemtype="h>p://schema.org/Person">

<span
itemprop="name">Rob
Marshall</span>

</div>


32

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Linked Data 33

Linked Open Data
(September 2011)


Linked Data: WHAT IS IT ABOUT? 34

•  The Web as one huge interconnected data graph
•  A Web of interlinked objects
•  Objects described in a standard, web enabled representation (RDF)
•  Each RDF document describes the characteristics of a single object,
and links to related objects
•  Each object has a unique web address (URI)
•  Objects are linked with other objects
-‐  Important: links to the same objects in different datasets (sameAs)
•  Guidelines for proper configuration of web servers to serve such
documents
•  Vocabularies should be reused!


Linked Data: WE TALK ABOUT OBJECTS 35

The DBpedia ontology, http://dbpedia.org
Wikipedia


Linked Data: OBJECTS ARE LINKED! Data

36

Mashups

Moving to England…
looking for a place to
leave

A unique information space

Available houses
Amenities Crime reports

Linked Data 37

•  Advantages:
•  No change to the publishing of the HTML documents
•  Data can be published by third party (e.g. DBpedia)
•  Data is interlinked
•  Disadvantages:
•  Web servers need to be configured to properly handle URIs that identify
concepts instead of documents
•  Search engines need to be extended to crawl Linked Data
•  Suitable vocabularies/ontologies are not always available
•  Data is not always easy to obtain
•  Data is not always linked to relevant data
•  Data is not always linked to HTML documents
-‐  Web mining: OBTAINING, STRUCTURING AND LINKING DATA

Methodological guidelines for publishing Linked Data 38

h>p://delicias.dia.ﬁ.upm.es/wiki/images/f/f6/04_MGLD.pdf


Vocabularies / Schemas / Ontologies 39

•  Search for suitable vocabularies
•  Reuse (if they exist)
•  Build new vocabularies based on existing ones (if they do not exist)


Obtaining and structuring data 40

•  From unstructured, semi-structured and structured data sources

Textual
documents
Spreadsheets

Natural Language Processing (NLP)
•  Entity extractors
•  Relation extractors NOR2O
http://oeg-dev.dia.fi.upm.es/nor2o/#download
TextRunner
http://openie.cs.washington.edu Cvs2rdf4LOD
http://logd.tw.rpi.edu/technology/csv2rdf4lod
YAGO: A Core of Semantic Knowledge
Unifying WordNet and Wikipedia, Suchanek et
al., WWW 2007. MappingMaster
http://protege.cim3.net/cgi-bin/wiki.pl?MappingMaster



Bringing
the
Deep
Web
to
the

Surface!

DataBases
WebAPis

Bizer
et
al.
The
RDF
Book
Mashups:

Whole
subarea
of
research
within
From
Web
APIs
to
a
Web
of
Data

the
SW
community
h>p://www.dvi.uni-‐kl.de/~grimnes/

2007/06/SFSW07Papers/6.pdf

RDB2RDF

h>p://www.w3.org/2001/sw/rdb2rdf

WebForms

ultrawrap

h>p://capsenta.com/ultrawrap

RDBToOnto
Madhavan
et
al.
Google's
Deep-‐Web

h>p://www.tao-‐project.eu/

Crawl.
VLDB
2008

researchanddevelopment/

h>p://www.cs.cornell.edu/~lucja/

demosanddownloads/
publicaYons/i03.pdf

RDBToOnto.html

Web Mining
The Semantic Web: From words to meanings


HTML
Tables
And
many
other
semi-‐structured
data

XML,
UML,
BibText,
Tags,
…

Cafarella
et
al.
WebTables:
ConverterToRDF

Exploring
the
Power
of
Tables
h>p://www.w3.org/wiki/ConverterToRdf

on
the
Web.
VLDB
2008

Other useful tools to obtain and structure data
Google
Refine

h>ps://code.google.com/p/google-‐refine

And
its
extension
RDF
Refine

h>p://refine.deri.ie

Google
Fusion
Tables

h>p://support.google.com/fusiontables


Linking Data 43

•  1. Identify suitable datasets as linking targets

Data
Hub

ckan.net

Geonames

DBpedia

•  2. Discover relationships between data items
owl:sameAs
owl:sameAs

http:// http://
http://mydataset/
sws.geonames.org/ dbpedia.org/
Madrid
6355233 resource/Madrid

Silk
Framework:
h>p://wifo5-‐03.informaYk.uni-‐mannheim.de/bizer/silk

Limes:
h>p://aksw.org/Projects/LIMES.html

h>p://delicias.dia.ﬁ.upm.es/wiki/images/f/f6/04_MGLD.pdf


Linking Data to HTML Documents: THE ANNOTATORS 44

•  The annotators extract entities (classes / individuals) and relations
from the text and link them to object URIs


45

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Obtaining, structuring and linking data: AN EXAMPLE 46

BEFORE LINKED DATA

Any material
about C++?...
Ufff...
Where to start?!

AFTER LINKED DATA

One unique
information
space


Obtaining, structuring and linking data: AN EXAMPLE 47

Use of Linked Data
1.  Select and extract educational
information from various sources
2.  Reuse well-known vocabularies to
describe and structure the previously
Fernandez
et
al.

(2011)
Linking
Data

Across
UniversiYes:
An
Integrated

extracted data
Video
Lectures
Dataset,
10th
3.  Link the educational material under a
InternaYonal
SemanYc
Web
common categorization scheme
Conference
(ISWC
2011),
Bonn


Specification: SELECTING & ANALYSING DATA 48

•  VideoLectures.net

Web scrapper
Study the structure of
HTML pages and use a
HTML parser to extract
the data of interest



•  YouTube University Channels
<entry gd:etag='W/"DkADSH47eCp7ImA9WhZWFEg."'>
<id>tag:youtube.com,2008:video:zZCaHSW88Ts</id>
<published>2011-02-18T11:41:08.000Z</published>
<updated>2011-05-15T10:19:39.000Z</updated>
<category scheme='http://gdata.youtube.com/schemas/2007/categories.cat'
term='Education' label='Education'/>
<category scheme='http://gdata.youtube.com/schemas/2007/keywords.cat'
term='Dr Barry Cooper'/>
<title>Intro to Professional Practice (Children & Families)</title>
<author> … </author>
<media:description … </media:description>
<media:keywords>…</media:keywords>
<media:thumbnail …/>
<yt:duration seconds='399'/>
<content …'/>

YouTube API: select the the fields of interest


•  OU Podcasts (data.open.ac.uk)

Already structured data: SPARQL query

Modeling: REUSING VOCABULARIES 51

Vocabularies selected to describe the data

•  Dublin Core I describe
•  http://dublincore.org/documents/dcmi-terms (dcterms) educational
material
•  FOAF
I describe people
•  http://xmlns.com/foaf/spec (foaf) and relations
•  The W3C ontology for media resources
•  http://www.w3.org/TR/mediaont-10 (ma)
We describe media
•  The Media Vocabulary resources
•  http://payswarm.com/vocabs/media (media)
•  The Nice Tag Ontology
•  http://ns.inria.fr/nicetag/2010/09/09/voc.html (nt) I describe tags


Modeling: REUSING VOCABULARIES 52

The selected base URI is: http://linkeduniversities.org

1.  VideoLectures objects are represented as media:Recording,
authors are represented as foaf:Person
2.  The video title is duplicated in the properties rdfs:label &
dcterms:title
4.  The set of tags and categories associated to a video is represented by
the nt:isRelatedTo property
5.  The assigned classification in the unified search space is represented by
the dcterms:subject property


RDF generation: CREATING OBJECTS 53

Type
Provenance
ID

http://linkeduniversities.org/video/CarnegieMellonU/youtube/B135229F3706D215

rdf:type media:Recording
media:download http://www.youtube.com/watch?v=TOTuStPIeFc&feature=youtube_gdata_player
dcterms:title CMU Football Engineering Summer 2008 Video
rdfs:label CMU Football Engineering Summer 2008 Video
dcterms:description Football […]Summer 2008 Video

foaf:thumbnail http://i.ytimg.com/vi/TOTuStPIeFc/3.jpg
media:duration 155
dcterms:isPart http://linkeduniversities.org/video/CarnegieMellonU/youtube/playlist/B135229F37

ma:publisher http://linkeduniversities.org/video/CarnegieMellonU/youtube/user/footballtracking
dcterms:published 2011-06-03T23:23:53.262Z

nt:isRelatedTo http://linkeduniversities.org/video/CarnegieMellonU/tag/sports
nt:isRelatedTo http://linkeduniversities.org/video/CarnegieMellonU/tag/football
dcterms:subject http://dmoz.org/Sports/Football/Rugby_Union
dcterms:subject http://linkeduniversities.org/video/CarnegieMellonU/dmoz/Sports/Football/Rugby_Union


Links Generation: SELECTING THE LINKING POINTS 54

•  A common categorization scheme:
1.  The International Press Telecommunications Council (IPTC)
http://www.iptc.org/site/NewsCodes/
2.  Library of Congress Subject Headings
http://id.loc.gov/authorities/about.html
3.  The Open Directory Project (DMOZ)
http://www.dmoz.org/rdf.html
4.  DBpedia Categories
http://dbpedia.org/About


Links Generation: EXPLOITING THE ANNOTATORS 55

(1) Extract the information
from the video lecture (4) Obtain the ODP document
classification

Reference/Knowledge_Management (id=495),
weight=0.71

(3) Provide the document to the
(2) Generate an textual document Textwise classification service


PUBLICATION 56

•  New LD educational dataset
•  More than 14,000 video lectures from 27 institutions


57

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Search engines: BETTER DATA VISUALISATION 58

•  2007 Yahoo! Presented
Search Monkey

•  2009 Google
announced
Rich Snippets

If search engines could understand the data inside the HTML pages they
would display such data in better ways: pictures, dates, prices, ratings, etc.

Search engines: BETTER CONTENT RETRIEVAL 59

2012 Google announced Knowledge Graph
http://googleblog.blogspot.co.uk/2012/05/
introducing-knowledge-graph-things-not.html


Search engines: NOT ALL IS ABOUT DOCUMENTS 60

•  Search engines oriented to concept-search vs. document search
FreeBase: In July 2010
Google bought Metaweb,
the company behind
freebase

Wolfram Alpha: Active
Powerset: Bought by since 2009
Microsoft in 2008


61

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Recommendations: FACEBOOK GRAPH SEARCH 62

Links among people, places,
music, and other objects can
lead to better content
recommendation


63

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


Open Government 64

•  Data.gov
•  Data.gov.uk
•  Many others…

•  Linking data lets more
complex queries and deeper
data analysis
•  Organizations can now easily
link to public government data
Research Funding Explorer •  Favour transparency

65

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


The Open University 66

OU public data sit in
OU public data sit in
different systems ––hard to
different systems hard to RAE

discover, obtain, integrate
discover, obtain, integrate
Data
from

by users
by users OpenLearn

Research

Content
ORO

Outputs

Archive
of

Library’s

Course

Exposed as linked data,
Exposed as linked data, Material

Catalogue

Of
Digital

our data interlink with each
our data interlink with each
Content

other and the external
other and the external data.gov.uk

world: become part of the
world: become part of the A/V
Material

Podcasts

“global data space” on the
“global data space” on the iTunesU

Web
Web
DBLP



•  The OU has been the first University to expose its data as linked data:
http://data.open.ac.uk
•  Now widely recognized as a critical step forward for the HE sector in
the UK (and worldwide)
•  Favor transparency and reuse of data, both externally and internally
•  Reduces cost of dealing with our own public data: integration and reuse by
design
•  Enable both new kinds of applications, and to make the ones that are already
feasible more cost effective



•  Linking Open Learn with relevant Podcasts



•  Exploring research communities


70

•  Linked Data
-‐  An example
•  Institutions
-‐  BBC


BBC: British Broadcasting Corporation 71

•  Historically when developing websites…
•  Hand-crafted customized sites
•  Only some programs could be covered
-‐  Shame considering they broadcast between 1,000 and
1,500 programs/day
•  All those sites were developed in isolation
-  Often not maintained
-  Often not persistent

Using Linked Data to build Websites more productively
http://www.slideshare.net/metade/linked-data-on-the-bbc


•  The Linked Data Website construction model
Domain-driven design
•  Identify the domain objects
and relations
•  Check the domain model with
users
•  Design your schema
•  Design your URIs
•  Define data you need to build
each of your pages
•  Automatically build up your
HTML pages by querying
these data and applying CSS
layouts


•  Programs
•  Starting with the WorldCup 2010… and now… •  Music
•  Artists
•  Animal life
•  …..


Thx! J 74

•  Questions, doubts?


Wm unit1.6-slides-semantic web-final

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