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 Consolidation
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. ARCOMEM architecture
Slide 3
Crawler
Cross Crawl Analysis
Online
Processing
Offline
Processing
Queue
Management
Application-Aware
Helper
Resource Selection
& Prioritization
Resource
Fetching
Intelligent
Crawl
Definition
Consolidation
Enrichment
GATE Offline Analysis
Social Web Analysis
GATE Online Analysis Social Web Analysis
Named Entity
Evol. Recog.
Extracted
SocialWeb
Information
Crawler
Cockpit
ARCOMEM
Storage
URLs
Relevance Analysis
&
Priorization
Image/Video Analysis
Twitter
Dynamics
WARC Export
WARC
Files
Applications
Broadcaster
Application
Parliament
Application
ARCOMEM system architecture foresees four processing
levels: crawler level, online processing level, offline
processing level and cross crawl analysis

4. 4
ETOE offline processing chain
The processing chain depicted here describes all components involved in
the offline processing of Web objects.

5. 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 5

6. ETOE extraction with GATE: an example
Slide 6
candidate multi-word term

7. Data consolidation & integration problem
Data extracted from different components or during
different processing cycles not aligned
=> consolidation, disambiguation & correlation required.
Slide 7
<Location>Greece</Location>
<Person>Venizelos</Person>
<Location>Griechenland</Location>
<Organisation>Greek Parliament</Organisation>
?

8. Data clustering & enrichment
Enrichment of entities with related references to Linked
Data, particularly reference datasets (DBpedia, Freebase, …)
=> use enrichments for correlation/clustering/consolidation
Slide 8

9. Enrichment with DBpedia & Freebase
• DBpedia and Freebase are particularly well-suited due to
their vast size, the availability of disambiguation techniques
which can utilise the variety of multilingual labels available
in both datasets for individual data items and the level of
inter-connectedness of both datasets, allowing the retrieval
of a wealth of related information for particular items.
• In the case of DBpedia, we make use of the DBpedia
Spotlight service which enables an approximate string
matching with adjustable confidence level in the interval
[0,1]. Experimentally, we set confidence to 0.6.
• For Freebase, we use structured queries, taking into
account entity types extracted by GATE.
9

10. <Event>Trichet warns of systemic debt crisis</Event>
<Person>Jean Claude Trichet</Person> <Organisation>ECB</Organisation>
Enrichment for clustering & correlation: example
Slide 10

11. <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 11

12. => 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 12

13. ARCOMEM entities and enrichments - graph
Slide 13
 Nodes: entities/events (blue), enrichments DBpedia (green), Freebase (orange)
 1013 clusters of correlated entities/events

14.  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 14

15. Clustering of entities via enrichment relatedness
Discovery of “related” entities by discovering related enrichments
(a) Retrieving possible paths between 2 enrichments (eg via RelFinder
http://www.visualdataweb.org/relfinder.php)
(b) Computation of relatedness measure (considering variables such as shortest
path, number of paths, relationship types, number of directly connected edges of
both enrichments…)
(c) Clustering enrichments (entities) which are above certain threshold
Slide 15

16. RDF schema for the Knowledge Base
16
 Relationships between ARCOMEM entities (ETOE etc) and enrichments
 RDF schema: http://www.gate.ac.uk/ns/ontologies/arcomem-data-
model.rdf

17. Enrichment evaluation results
 Manual evaluation of 240 enrichment-entity pairs
 Available scores: 1 (correct), 0 (incorrect), 0.5 (vague or
ambiguous relationship)
Slide 17
Entity Type Average score
DBpedia
Average score
Freebase
Average Score
Total
arco:Event 0.71 0.71
arco:Location 0.81 0.94 0.88
arco:Money 0.67 0.67
arco:Organization 0.93 1 0.97
arco:Person 0.9 0.89 0.89
arco:Time 0.74 0.74
Total 0.79 0.94 0.87

18. Further reading
• Entity Extraction and Consolidation for Social Web Content Preservation. S.
Dietze, D. Maynard, E. Demidova, T. Risse, W. Peters, K. Doka und Y.
Stavrakas, SDA, volume 912 of CEUR Workshop Proceedings, page 18-29.
CEUR-WS.org, (2012)
• Can entities be friends? B. P. Nunes , R. Kawase, S. Dietze, D. Taibi, M. A.
Casanova, W. Nejdl Boston, US, 2012. Web of Linked Entities
(WOLE2012), Workshop at The 11th International Semantic Web Conference
(ISWC2012).
• Combining a co-occurrence-based and a semantic measure for entity linking. B.
P. Nunes, S. Dietze, M. A. Casanova, R. Kawase, B. Fetahu, W. Nejdl. 2013.
ESWC 2013 - 10th Extended Semantic Web Conference.
• Linked data - The Story So Far. Biser, C., Heath, T. and Berners-Lee, T.
2009, Special Issue on Linked data, International Journal on Semantic Web and
Information Systems (IJSWIS).
Slide 18

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