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

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

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

The Web of DataDigital Enterprise Research Institute www.deri.ie Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. Enabling Networked Knowledge

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

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

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

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

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

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

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

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

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

Aggregated, Interoperable and Multi- Domain User Profiles for the Social WebDigital Enterprise Research Institute www.deri.ie15 Enabling Networked Knowledge

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

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

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

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

by Fabrizio Orlandi on Jul 20, 2012

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

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