Multi-Source Provenance-Aware User Interest Profiling on the Social Semantic Web
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Multi-Source Provenance-Aware User Interest Profiling on the Social Semantic Web

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UMAP 2012 Doctoral Consortium

UMAP 2012 Doctoral Consortium

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Multi-Source Provenance-Aware User Interest Profiling on the Social Semantic Web Presentation Transcript

  • 1. Digital Enterprise Research Institute www.deri.ie Multi-Source Provenance-Aware User Interest Profiling on the Social Semantic Web Fabrizio Orlandi Doctoral Consortium – UMAP 2012 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Enabling Networked Knowledge
  • 2. Research GoalDigital Enterprise Research Institute www.deri.ie  Improve the current user interest profiling techniques leveraging:  Linked Data,  Provenance of Data,  the Social Semantic Web.2 Enabling Networked Knowledge
  • 3. The Web of DataDigital Enterprise Research Institute www.deri.ie Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. Enabling Networked Knowledge
  • 4. The Web of DataDigital Enterprise Research Institute www.deri.ie db:Ferrari db:Formula_1 dbo:wikiPageWikiLink dbo:wikiPageWikiLink db:Gilles_Villeneuve dbo:birthPlace db:Quebec dbp:largestcity db:Montreal Enabling Networked Knowledge
  • 5. Research AreasDigital Enterprise Research Institute www.deri.ie  Social media integration and interoperability  How to extract and aggregate relevant user information from social media websites and make it available following the Linked Data principles?  How adaptive should be a user profiling algorithm according to the type of social media?  Provenance of data  What is the role of provenance on the Social Web and on the Web of Data and how to use it for user profiling?  How dependent are profiling algorithms from the origin, history and types of user activities on Social Web and how to adapt to it?  The Web of Data for interest profiling  How to use the Web of Data and semantic technologies to enrich user profiles?  How to leverage the Web of Data for different ranking strategies of user interests?5 Enabling Networked Knowledge
  • 6. Challenges – 1Digital Enterprise Research Institute www.deri.ie  Information on the Social Web is stored in isolated data silos on heterogeneous and disconnected social media websites6 http://www.w3.org Enabling Networked Knowledge
  • 7. Challenges – 1Digital Enterprise Research Institute www.deri.ie  User profiles should be represented in an interoperable way in order to exchange information across different systems7 [image: U. Bojārs, A. Passant, J. Breslin] Enabling Networked Knowledge
  • 8. Research QuestionsDigital Enterprise Research Institute www.deri.ie  Social media integration and interoperability  How to extract and aggregate relevant user information from social media websites and make it available following the Linked Data principles?  How adaptive should be a user profiling algorithm according to the type of social media?  Provenance of data  What is the role of provenance on the Social Web and on the Web of Data and how to use it for user profiling?  How dependent are profiling algorithms from the origin, history and types of user activities on Social Web and how to adapt to it?  The Web of Data for interest profiling  How to use the Web of Data and semantic technologies to enrich user profiles?  How to leverage the Web of Data for different ranking strategies of user interests?8 Enabling Networked Knowledge
  • 9. Challenges – 2Digital Enterprise Research Institute www.deri.ie  Lack of provenance on the Web of Data: datasets on the Social Web are often the result of data mashups or collaborative user activities9 Enabling Networked Knowledge
  • 10. Research QuestionsDigital Enterprise Research Institute www.deri.ie  Social media integration and interoperability  How to extract and aggregate relevant user information from social media websites and make it available following the Linked Data principles?  How adaptive should be a user profiling algorithm according to the type of social media?  Provenance of data  What is the role of provenance on the Social Web and on the Web of Data and how to use it for user profiling?  How dependent are profiling algorithms from the origin, history and types of user activities on Social Web and how to adapt to it?  The Web of Data for interest profiling  How to use the Web of Data and semantic technologies to enrich user profiles?  How to leverage the Web of Data for different ranking strategies of user interests?10 Enabling Networked Knowledge
  • 11. Challenges – 3Digital Enterprise Research Institute www.deri.ie  The Web of Data: a continuously evolving “open corpus”11 LOD Cloud by R. Cyganiak and A. Jentzsch Enabling Networked Knowledge
  • 12. Research QuestionsDigital Enterprise Research Institute www.deri.ie  Social media integration and interoperability  How to extract and aggregate relevant user information from social media websites and make it available following the Linked Data principles?  How adaptive should be a user profiling algorithm according to the type of social media?  Provenance of data  What is the role of provenance on the Social Web and on the Web of Data and how to use it for user profiling?  How dependent are profiling algorithms from the origin, history and types of user activities on Social Web and how to adapt to it?  The Web of Data for interest profiling  How to use the Web of Data and semantic technologies to enrich user profiles?  How to leverage the Web of Data for different ranking strategies of user interests?12 Enabling Networked Knowledge
  • 13. OutlineDigital Enterprise Research Institute www.deri.ie 1 3 2 The user profiling data process: 1. from user activities on heterogeneous social media websites, 2. to their provenance representation,13 3. to the data aggregation, analysis and integration with the Web of Data. Enabling Networked Knowledge
  • 14. Work doneDigital Enterprise Research Institute www.deri.ie Month: Semantic integration of social networking platforms (the wikis use case) 1st – 6th Semantic representation and management of provenance on the Social Web and the Web of Data (DBpedia) 6th – 18th Aggregated, Interoperable and Multi-Domain User Profiles of Interests for the Social Web 18th – 24th Personalized Filtering of Privacy Aware and Faceted the Twitter Stream User-Profile Management14 Enabling Networked Knowledge
  • 15. Aggregated, Interoperable and Multi- Domain User Profiles for the Social WebDigital Enterprise Research Institute www.deri.ie15 Enabling Networked Knowledge
  • 16. Open QuestionsDigital Enterprise Research Institute www.deri.ie  How adaptive should be a user profiling algorithm according to the type of social media?  What are the differences between extracting user interests on Microblogs, Wikis, Social Networking sites, etc.?  How can a general purpose user interesting profiling algorithm adapt to it?  How dependent are profiling algorithms from the origin, history and types of user activities on Social Web and how to adapt to it?  What are the different types of activities that users perform on the Social Web expressing personal interest and how to weight them?  How does detailed provenance information about user activities help in creating more accurate and fine-grained profiles?  How to leverage the Web of Data for different ranking strategies of user interests?  How relevant are the collected interests for a user profile and what are their relations with other concepts on the Web of Data?16 Enabling Networked Knowledge
  • 17. Future WorkDigital Enterprise Research Institute www.deri.ie ■ User profiling on Wikipedia analysing authorship and contributions for DBpedia statements and Wikipedia articles. ■ Test of user interest profiling strategies on different scenarios (Microblogs, Wikis, etc.) ■ Integration and enrichment of the semantic user profiles generated with the Web of Data and other Social Media ■ Evaluation of the generated user profiles17 Enabling Networked Knowledge
  • 18. ThanksDigital Enterprise Research Institute www.deri.ie Contacts: http://bit.ly/M7hvbX fabrizio.orlandi@deri.org @BadmotorF Thanks to: Alexandre Passant - @terraces John Breslin - @johnbreslin18 Enabling Networked Knowledge