Arcomem training enrichment_advanced

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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.

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Arcomem training enrichment_advanced

  1. 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. 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. 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. 4 ETOE offline processing chain The processing chain depicted here describes all components involved in the offline processing of Web objects.
  5. 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. 6. ETOE extraction with GATE: an example Slide 6 candidate multi-word term
  7. 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. 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. 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. 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. 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. 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. 13. ARCOMEM entities and enrichments - graph Slide 13  Nodes: entities/events (blue), enrichments DBpedia (green), Freebase (orange)  1013 clusters of correlated entities/events
  14. 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. 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. 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. 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. 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. 19. THANK YOU CONTACT DETAILS Dr. Elena Demidova L3S Research Center +49 511 762 17732 demidova@L3S.de www.arcomem.eu

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