UMAP 2012 Doctoral Consortium

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 Goal
Digital Enterprise Research Institute www.deri.ie
 Improve the current user interest profiling
techniques leveraging:
 Linked Data,
 Provenance of Data,
 the Social Semantic Web.
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3. The Web of Data
Digital Enterprise Research Institute www.deri.ie
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
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4. The Web of Data
Digital 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
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5. Research Areas
Digital 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?
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6. Challenges – 1
Digital Enterprise Research Institute www.deri.ie
 Information on the Social Web is stored in isolated data silos
on heterogeneous and disconnected social media websites
6 http://www.w3.org
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7. Challenges – 1
Digital Enterprise Research Institute www.deri.ie
 User profiles should be represented in an interoperable way
in order to exchange information across different systems
7 [image: U. Bojārs, A. Passant, J. Breslin]
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8. Research Questions
Digital 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?
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9. Challenges – 2
Digital 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 activities
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10. Research Questions
Digital 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?
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11. Challenges – 3
Digital Enterprise Research Institute www.deri.ie
 The Web of Data: a continuously evolving “open corpus”
11 LOD Cloud by R. Cyganiak
and A. Jentzsch
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12. Research Questions
Digital 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?
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13. Outline
Digital 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.
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14. Work done
Digital 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 Management
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15. Aggregated, Interoperable and Multi-
Domain User Profiles for the Social Web
Digital Enterprise Research Institute www.deri.ie
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16. Open Questions
Digital 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?
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17. Future Work
Digital 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 profiles
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18. Thanks
Digital Enterprise Research Institute www.deri.ie
Contacts:
http://bit.ly/M7hvbX
fabrizio.orlandi@deri.org
@BadmotorF
Thanks to:
Alexandre Passant - @terraces
John Breslin - @johnbreslin
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