multi-source provenance-aware user interest profiling on the social semantic web

Copyright 2011 Digital Enterprise Research Institute. All rights reserved.

Digital Enterprise Research Institute www.deri.ie

Enabling Networked Knowledge

Multi-Source Provenance-Aware

User Interest Profiling

on the Social Semantic Web

Fabrizio Orlandi

Doctoral Consortium – UMAP 2012



Research Goal

Improve the current user interest profiling

techniques leveraging:

Linked Data,

Provenance of Data,

the Social Semantic Web.

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The Web of Data

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.



The Web of Data

db:Montreal

db:Quebec

db:Gilles_Villeneuve

db:Ferrari db:Formula_1

dbo:wikiPageWikiLinkdbo:wikiPageWikiLink

dbo:birthPlace

dbp:largestcity



Research Areas

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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Challenges – 1

Information on the Social Web is stored in isolated data silos

on heterogeneous and disconnected social media websites

http://www.w3.org6



Challenges – 1

User profiles should be represented in an interoperable way

in order to exchange information across different systems

[image: U. Bojārs, A. Passant, J. Breslin]7



Research Questions


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?




8



Challenges – 2

Lack of provenance on the Web of Data:

datasets on the Social Web are often the result of data

mashups or collaborative user activities

9



Research Questions


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?




10



Challenges – 3

The Web of Data: a continuously evolving “open corpus”

LOD Cloud by R. Cyganiak

and A. Jentzsch11

http://richard.cyganiak.de/

http://richard.cyganiak.de/

http://www.wiwiss.fu-berlin.de/en/institute/pwo/bizer/team/JentzschAnja.html

http://www.wiwiss.fu-berlin.de/en/institute/pwo/bizer/team/JentzschAnja.html



Research Questions


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?


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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Outline

The user profiling data process:

1. from user activities on heterogeneous social media websites,

2. to their provenance representation,

3. to the data aggregation, analysis and integration with the Web of Data.

12

3

13



Work done

Aggregated, Interoperable and Multi-Domain

User Profiles of Interests for the Social Web

Privacy Aware and Faceted

User-Profile Management

Personalized Filtering of

the Twitter Stream

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Semantic integration of social networking

platforms (the wikis use case)

Semantic representation and management of provenance on the

Social Web and the Web of Data (DBpedia)

Month:

1st – 6th

6th – 18th

18th – 24th



Aggregated, Interoperable and Multi-

Domain User Profiles for the Social Web

15



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Open Questions

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?



Future Work

■ 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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Thanks

Contacts:

http://bit.ly/M7hvbX

[email protected]

@BadmotorF

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Thanks to:

Alexandre Passant - @terraces

John Breslin - @johnbreslin

mailto:[email protected]

multi-source provenance-aware user interest profiling on the social semantic web

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