tellmefirst - a knowledge domain discovery framework

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TellMeFirst Giuseppe Futia 1 , Antonio Vetrò 1 , Giuseppe Rizzo 2 A Knowledge Domain Discovery Framework THE HAGUE, NETHERLANDS – Feb 12th 2016 1- Nexa Center for Internet and Society, DAUIN, Politecnico di Torino 2- Istituto Superiore Mario Boella (ISMB)

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TellMeFirst

Giuseppe Futia1, Antonio Vetrò1, Giuseppe Rizzo2

A Knowledge Domain Discovery Framework

THE HAGUE, NETHERLANDS – Feb 12th 2016

1- Nexa Center for Internet and Society, DAUIN, Politecnico di Torino 2- Istituto Superiore Mario Boella (ISMB)

Nexa Center forInternet & Society

2

Interdisciplinary Research

Digital Culture

Support to Policy

Community

http://nexa.polito.it/

@nexacenter

What is TellMeFirst and how it works

How we build a generalist training set based on DBpedia and

Wikipedia

What is a domain training set (wrt the generalist one)

How we create a domain training set using a configurable pipeline

Agenda

3

What is TellMeFirst andhow it works

4

5

tellmefirst.polito.it

6

How TellMeFirst works

7

How TellMeFirst works

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TellMeFirst Classifier

TellMeFirst exploits an approach where the training set based on

DBpedia and Wikipedia is compared with the target document

In the training set, each DBpedia entity (i.e., Barack Obama) is

represented by all the Wikipedia paragraphs in which it appears

as wikilink (http://en.wikipedia.org/wiki/Barack_Obama)

A vector distance metric is used to understand how much a

Wikipedia paragraph is similar to the target document (Mendes,

2011)

How we build a generalist training set based on DBpedia and Wikipedia

9

Traditional approach(based on DBpedia Spotlight)

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Traditional approach(based on DBpedia Spotlight)

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The DBpedia Extractor

It takes as input some datasets built through the DBpedia Information

Extraction Framework (such as labels, redirects, disambiguations)

The output is a list of “good” URIs that effectively represent entities

(avoiding disambiguations and redirects pages)

The DBpedia/Wikipedia Mapper

It maps “good” URIs on the dump of Wikipedia and then it creates a

Lucene Index that defines the training set

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Training set - A Lucene Index

What is a domain training set

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Domain training set

It contains a subset of DBpedia entities indexed in the generalist

training set

It is defined according to the domain of documents that you need

to classify

It is build through a software component properly driven by

SPARQL queries and advanced services (i.e., Linked Data

Recommenders), to create a new list of “good” URIs

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How we build a domaintraining set using a configurable pipeline

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Traditional approach (recap)

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Domain training set - Pipeline (i)

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Domain training set - Pipeline (ii)

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Domain Index - Pipeline (iii)

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Domain Engine - SPARQL

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Domain Engine - LDR

First implementation: Linked Data Recommender (LDR)

developed by the SoftEng group of the Politecnico di

Torino

Get all DBpedia categories from a DBpedia entity

Get DBpedia entities related to a specific DBpedia entity and a DBpedia

category

Pipeline: get new entities with LDR from resources

retrieved with SPARQL queries21

Example - ColosseumThe Colosseum or Coliseum (/kɒləˈsiːəm/ kol-ə-see-əm), also known as the

Flavian Amphitheatre (Latin: Amphitheatrum Flavium; Italian: Anfiteatro Flavio

[amfiteˈaːtro ˈflaːvjo] or Colosseo [kolosˈsɛːo]), is an oval amphitheatre in the

centre of the city of Rome, Italy. Built of concrete and sand, it is the largest

amphitheatre ever built and is considered one of the greatest works of

architecture and engineering ever.

The Colosseum is situated just east of the Roman Forum. Construction began

under the emperor Vespasian in 72 AD, and was completed in 80 AD under his

successor and heir Titus. Further modifications were made during the reign of

Domitian (81–96). These three emperors are known as the Flavian dynasty, and

the amphitheatre was named in Latin for its association with their family name

(Flavius).

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Colosseum - Generalist training set

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Colosseum - Domain training set

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Comparison of results (i)

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Titus, Vespasian, and Domitian are identified through the

generalist training set and are directly mentioned in the

text

Arch of Titus, Temple of Vespasian and Titus, obtained with

the domain training set, are related to emperors

mentioned in the previous point, but refer to the cultural

heritage of the Ancient Rome

Comparison of results (ii)

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Flavian dynasty and Flavia entities are mentioned in the text,

but they are not so relevant for the cultural heritage

domain

The Great Fire of Rome is not strictly related to the entities

mentioned in the text, but it is relevant from an historical

point of view

Wrap up

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TellMeFirst is a tool for classifying and enriching textual

documents using a training set based on DBpedia and

Wikipedia

We are capable to build a training set for TellMeFirst with a

configurable pipeline to get a subset of all DBpedia entities

Driving this configurable pipeline, we are able to create a training

set for a specific knowledge domain (such as cultural heritage)

Future developments

Define a training set for classifying scientific publications

available in Open Access

Build a GUI in order to enable domain experts to create a

domain training set, without a specific knowledge of

Linked Data framework

We are open to collaborations on TellMeFirst!

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Acknowledgments

●Joint Open Lab of Telecom Italia

(http://www.telecomitalia.com/tit/it/innovazione/

i-luoghi-della-ricerca/joint-open-labs.html)

●Software Engineering Research Group (DAUIN),

Politecnico di Torino (http://softeng.polito.it/)

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• Giuseppe Futia– Mail: [email protected]– Twitter: @giuseppe_futia

• Antonio Vetrò– Mail: [email protected]– Twitter: @phisaz

• Giuseppe Rizzo– Mail: [email protected]– Twitter: @giusepperizzo

Contacts

Appendix

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