Learning with the Web. Structuring data to ease machine understanding

Learning with the
Web
Structuring data to ease
machine understanding
http://twitter.com/giusepperizzo

July 11th, 2013 Università di Torino, Italy 2/44
Google
Knowledge
Graph
Viewer

Google Knowledge Graph

The Google Knowledge Graph bulk:
encyclopedic sources

Web community has highlithed the road,
but ...

Vast wealth of unstructured data
“80% of data on the Web and on internal
corporate intranets is unstructured"
“80% of data on the Web and on internal
corporate intranets is unstructured”
“Semantic Web and Information Extraction Workshop”, SWAIE
at RANLP2013

The entire digital universe, going to
be part of the Web
“unstructured data will account for 90 percent of
all data created in the next decade”
IDC IVIEW, “Extracting Value from Chaos”, June 2011

Structured means
making those
resources available to be easily processed
by machines

A Web of Linked Entities
http://wole2013.eurecom.fr
http://wole2012.eurecom.fr
➢ GGG (global giant graph)
http://goo.gl/fH3h
➢ Nodes are Web entities
➢ Entities provide disambiguation
pointers
➢ Entities can be univocally referred
(disambiguated)
➢ Entities as centroids for topic
generation and undestanding

Chapter 1:
Named Entity Recognition (NER)
and
Named Entity Linking (NEL)

I want to book a room in an hotel located in the
heart of Paris, just a stone’s throw from the
Eiffel Tower
Eric Charton, “Named Entity Detection and
Entity Linking in the Context of Semantic Web:
Exploring the ambiguity question”

Part of Speech
I
want
to
book
a
room
in
..
Paris
PRP
VBP
TO
VB
DT
NN
IN
..
NNP
I
want
to
book
a
room
in
..
Paris
NER: What is Paris?
NEL: Which Paris are we
talking about?

What is Paris?
Type ambiguity
asteroid location/city film

Entity recognition
I
want
to
book
a
room
in
..
Paris
PRP
VBP
TO
VB
DT
NN
IN
..
NNP
I
want
to
book
a
room
in
..
Paris
O
O
O
O
O
O
O
..
LOC

NER: State of the art
➢ CRFs (Conditional Random Fields)
➢ FSM (Finite-State Machine)
➢ HMM (Hidden Markov Model)
➢ Gazetteers
➢ Wikipedia/DBpedia
➢ In-house dictionaries

Which Paris?
Name ambiguity
Paris, Kentucky Paris, Maine Paris, Tennessee
Paris, France Paris, Ontario
Paris, Idaho

Entity linking
I
want
to
book
a
room
in
..
Paris
PRP
VBP
TO
VB
DT
NN
IN
..
NNP
I
want
to
book
a
room
in
..
Paris
O
O
O
O
O
O
O
..
LOC
O
O
O
O
O
O
O
..
http://en.wikipedia.org/wiki/Paris

Ambiguity resolution: linking to an
external knowledge base
➢ Gigaword Corpus
➢ In-house dataset
➢ LOD dataset
➢ DBLP
➢ ACM
➢ BBC
➢ ...

NEL: State of the art
➢ Clustering
➢ Vector Space Model (Cosine similarity or
Maximum Entropy) – it requires a priori
knowledge of the spotted entities
➢ Conditional probability – it requires a priori
knowledge of the spotted entities
➢ Dictionaries
➢ In-house dataset

Processing natural language texts
➢ Several attempts from the Web community to
structure the large wealth of data available
➢ Numerous off-the-shelf systems (commercial, and
academic) that perform the NER+NEL chain
➢ AlchemyAPI
➢ DBpedia Spotlight
➢ Wikimeta
➢ TextRazor
➢ Stanford CRF
➢ ...

The NERD initiative
http://nerd.eurecom.fr

Combination of off-the-shelf systems
and properly trained CRFs

The strength of this approach lies in the fact that
the supported off-the-shelf systems have access
to large knowledge bases of entities such as
DBpedia and Freebase, while CRFs are domain
specific

Diversity
Alchemy
API
DBpedia
Spotlight
Extractiv Lupedia Open
Calais
Saplo Semi
Tags
Wikimeta Yahoo! Zemanta
Classification
schema
Alchemy DBpedia
FreeBase
Scema.org
Extractiv DBpedia
LinkedM
DB
Open
Calais
Saplo ConLL-
3
ESTER Yahoo FreeBase
Number of
classes
324 320 34 319 95 5 4 7 13 81

NERD Ontology
NERD type Occurrence
Person 10
Organization 10
Country 6
Company 6
Location 6
Continent 5
City 5
RadioStation 5
Album 5
Product 5
... ...
The NERD ontology has been integrated in the NIF project, a EU FP7 in the
context of the LOD2: Creating Knowledge out of Interlinked Data

Learning with the Web
➢ FSM-core based
➢ combination of the NERD supported off-the-shelf
systems
➢ ML-core based
➢ combination of the NERD supported off-the-shelf
systems
– and a CRF, properly trained with the given corpus

Challenges and benchmark

ETAPE 2012 - Entity Extraction
Challenge
➢ French transcripts of radio and video programs
➢ Challenge objective: entity typing
➢ Sumitted system:
➢ FSM-core based
➢ Given annotation priority to the systems that have
fine grained classification schemes
➢ Ranked 7th/7

#MSM'13 - Concept Extraction
Challenge
➢ English Twitter microposts
➢ Challenge objective: entity typing
➢ Submitted system:
➢ ML-core based: SVM
➢ Features = linguistic features (some of them are
capitalization, 3 chars of prefix and suffix, POS), output
of a CRF properly trained with the challenge training
dataset, outputs of the off-the-shelf systems
➢ Ranked 2nd/22

CoNLL-2003
➢
English newswire corpus
➢
Benchmark objective: entity typing
➢
System:
➢
ML-core based: SVM and NB
➢
Features = linguistic features (some of them are capitalization, 3
chars of prefix, 3 chars of suffix, POS), output of a CRF properly
trained with the challenge training dataset, output of the
off-the-shelf systems
➢
Results: outperformed significantly the performances of all
the systems (off-the-shelf) used as inputs and the Stanford
CRF properly trained with the CoNLL-2003 training corpus

TAC KBP 2011
➢ English newswire corpus
➢ Benchmark objective: entity linking
➢ System:
➢ FSM-core based
➢ Features: outputs of the off-the-shelf systems,
harmonized with the Gigaword corpus
ongoing

NERD in action
http://nerd.eurecom.fr/annotation/247957

Chapter 2:
Annotating streams of
heterogeneous data coming from
social platforms for topic
generation

The Social Web is growing fast and is becoming
of a crucial importance for research and
companies

Social Web = Big Data
Gartner “3V” definition: Volume, Velocity, Variety
of microposts

Microposts
➢ Short (~140 characters) and informal text
➢ Grammar free text
➢ Slang
➢ Media items
➢ Picture
➢ Video

Can we make sense out of the massive and
rapidly changing amount of information shared in
the Social Web?

Live topic generation
http://youtu.be/8iRiwz7cDYY

http://mediafinder.eurecom.fr

Tracking and analyzing an event
➢ 1 week period
➢ We collected microposts enclosed with pictures
➢ We followed the 2013 Italian Election
➢ We compared the results with the articles
published in those days on famous newspapers
http://youtu.be/jIMdnwMoWnk

http://mediafinder.eurecom.fr/story/elezioni2013

Outlook: an entity graph from the open and
Social Web

Thanks for your time and attention
http://www.slideshare.net/giusepperizzo

Do you have any questions?

Learning with the Web. Structuring data to ease machine understanding

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