This presentation on data enrichment is part of the ARCOMEM training curriculum. Feel free to roam around or contact us on Twitter via @arcomem to learn more about ARCOMEM training on archiving Social Media.

1. Entity Enrichment and Clustering
in ARCOMEM
Elena Demidova1,
including slides by: Stefan Dietze1, Diana Maynard2, Thomas Risse1, Wim Peters2,
Katerina Doka3, Yannis Stavrakas3
1 L3S Research Center, Hannover, Germany
2 University Sheffield, UK
3 IMIS, RC ATHENA, Athens, Greece

2. 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

3. 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

4. ETOE extraction with GATE: an example
Slide 4
candidate multi-word term

5. 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>
?

6. Data enrichment & clustering
Enrichment of entities with related references to Linked
Data, particularly reference datasets (DBpedia, Freebase, …)
=> use enrichments for clustering/correlation/consolidation
Slide 6

7. <Event>Trichet warns of systemic debt crisis</Event>
<Person>Jean Claude Trichet</Person> <Organisation>ECB</Organisation>
Enrichment for clustering & correlation: example
Slide 7

8. <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

9. => dbpprop:office dbpedia:President_of_the_European_Central_Bank
dbpedia:Governor_of_the_Banque_de_France
=> dcterms:subject category:Living_people
category:Karlspreis_recipients
category:Alumni_of_the_École_Nationale_d'Administration
category: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

10. ARCOMEM entities, enrichments & clusters
Slide 10
 Nodes: entities/events (blue), enrichments DBpedia (green), Freebase (orange)
 1013 clusters of correlated entities/events
Cluster built around
enrichment db:Market

11.  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
Cluster built around
enrichment db:Market
Cluster expansion

12. THANK YOU
CONTACT DETAILS
Dr. Elena Demidova
L3S Research Center
+49 511 762 17732
demidova@L3S.de
www.arcomem.eu