learning with the web. structuring data to ease machine understanding

Learning with the WebStructuring data to ease machine understanding

http://twitter.com/giusepperizzo

http://twitter.com/giusepperizzo

July 11th, 2013 Università di Torino, Italy 2/44

GoogleKnowledge 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

http://wole2013.eurecom.fr/

http://wole2012.eurecom.fr/

http://goo.gl/fH3h


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 recognitionI

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, OntarioParis, Idaho


Entity linkingI

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

➢ Wikipedia/DBpedia➢ 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 ➢ Wikipedia/DBpedia➢ 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

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

AlchemyAPI

DBpedia Spotlight

Extractiv Lupedia OpenCalais

Saplo SemiTags

Wikimeta Yahoo! Zemanta

Classificationschema

Alchemy DBpediaFreeBaseScema.org

Extractiv DBpediaLinkedM

DB

OpenCalais

Saplo ConLL-3

ESTER Yahoo FreeBase

Number of classes

324 320 34 319 95 5 4 7 13 81


NERD OntologyNERD 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

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://youtu.be/8iRiwz7cDYY


http://mediafinder.eurecom.fr

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://youtu.be/jIMdnwMoWnk

July 11th, 2013 Università di Torino, Italy 41/44http://mediafinder.eurecom.fr/story/elezioni2013

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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