Arcomem training enrichment_beginner

Entity Enrichment and Consolidationin ARCOMEM

Elena Demidova1,

including slides by: Stefan Dietze1, Diana Maynard2, Thomas Risse1, Wim Peters2, Katerina Doka3, Yannis Stavrakas3

1 L3S Research Center, Hannover, Germany2 University Sheffield, UK3 IMIS, RC ATHENA, Athens, Greece

The ARCOMEM approach

• Make use of the Social Web– Huge source of user generated content– Wide range of articulation methods

From simple „I like it“-Buttons to complete articles– Represents the diversity of opinions of the public

• User activities often triggered by – Events and related entities

(e.g. Sport Events, Celebrations, Crises, News Articles, Persons, Locations)

– Topics (e.g. Global Warming, Financial Crisis, Swine Flu)

A semantic-aware and socially-driven preservation model is a natural way to go

Slide 2

The extraction components for text

Aim Extraction of Entities, Topics, Events and Opinions (ETOEs) from

Web Pages Social Web (Twitter, YouTube, Facebook, …)

Challenges Entity recognition from degraded input sources (tweets etc)

Advancing state of the art NLP and text mining Dynamics detection: evolution of terms/entities

Semantic representation of Web objects and entities Appropriate RDF schemas for ETOE and Web objects Exploiting (Linked Open) Web data to enrich extracted ETOE

Entity classification (into events, locations, topics etc) & consolidation

Slide 3

ETOE extraction with GATE: an example

Slide 4

candidate multi-word term

Data consolidation & integration problem

Data extracted from different components or during different processing cycles not aligned => consolidation, disambiguation & correlation required.

Slide 5

<Location>Greece</Location><Person>Venizelos</Person> <Location>Griechenland</Location>

<Organisation>Greek Parliament</Organisation>

?

Data clustering & enrichmentEnrichment of entities with related references to Linked Data, particularly reference datasets (DBpedia, Freebase, …)=> use enrichments for correlation/clustering/consolidation

Slide 6

<Event>Trichet warns of systemic debt crisis</Event>

<Person>Jean Claude Trichet</Person> <Organisation>ECB</Organisation>

Enrichment for clustering & correlation: example

Slide 7

<Enrichment>http://dbpedia.org/resource/Jean-Claude_Trichet</Enrichment>

<Enrichment>http://dbpedia.org/resource/ECB</Enrichment>

<Event>Trichet warns of systemic debt crisis</Event>

<Person>Jean Claude Trichet</Person> <Organisation>ECB</Organisation>

Enrichment for clustering & correlation: example

Slide 8

=> dbpprop:office dbpedia:President_of_the_European_Central_Bankdbpedia:Governor_of_the_Banque_de_France

=> dcterms:subject category:Living_peoplecategory:Karlspreis_recipientscategory:Alumni_of_the_École_Nationale_d'Administrationcategory:People_from_Lyon…

<Enrichment>http://dbpedia.org/resource/Jean-Claude_Trichet</Enrichment>

<Enrichment>http://dbpedia.org/resource/ECB</Enrichment>

<Event>Trichet warns of systemic debt crisis</Event>

<Person>Jean Claude Trichet</Person> <Organisation>ECB</Organisation>

Enrichment for clustering & correlation: example

Slide 9

ARCOMEM entities and enrichments - graph

Slide 10

Nodes: entities/events (blue), enrichments DBpedia (green), Freebase (orange)

1013 clusters of correlated entities/events

Nodes: entities/events (blue), enrichments DBpedia (green), Freebase (orange)

1013 clusters of correlated entities/events => cluster expansion by considering related enrichments

ARCOMEM entities and enrichments - graph

Slide 11

THANK YOUCONTACT DETAILS

Dr. Elena DemidovaL3S Research Center+49 511 762 17732

demidova@L3S.dewww.arcomem.eu