We propose a framework to address an important issue in the context of the ongoing adoption of the "Web 2.0" in science and research, often referred to as "Science 2.0" or "Research 2.0". A growing number of people are linked via acquaintances and online social networks such as Twitter1allows indirect access to a huge amount of ideas. These ideas are contained in a massive human information flow. That users of these networks produce relevant data is being shown in many studies. The problem however lies in discovering and verifying such a stream of unstructured data items. Another related problem is locating an expert that could provide an answer to a very specific research question. We are using semantic technologies (RDF, SPARQL), common vocabularies(SIOC, FOAF, SWRC6) and Linked Data (DBpedia, GeoNames, CoLinDa) to extract and mine the data about scientific events out of context of microblogs. Hereby we are identifying persons and organization related to them based on entities of time, place and topic. The framework provides an API that allows quick access to the information that is analyzed by our system. As a proof-of-concept we explain, implement and evaluate such a researcher profiling use case. It involves the development of a framework that focuses on the proposition of researches based on topics and conferences they have in common. This framework provides an API that allows quick access to the analyzed information. A demonstration application: "Researcher Affinity Browser" shows how the API supports developers to build rich internet applications for Research 2.0. This application also introduces the concept "affinity" that exposes the implicit proximity between entities and users based on the content users produced. The usability of a demonstration application and the usefulness of the framework itself are investigated with an explicit evaluation questionnaire. This user feedback led to important conclusions about successful achievements and opportunities to further improve this effort.

1. Semantically Driven Social Data
Aggregation Interfaces for Research 2.0
Laurens De Vocht
Selver Softic
Martin Ebner
Herbert Mühlburger
http://www.semanticprofiling.net
September 7, 2011

2. Agenda
‣Problem Statement
‣Social Semantic Web
‣Solution
‣Evaluation
‣Conclusion
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3. Problem Statement: Definitions
Profiling
“Inferring unobser vable information about users from
observable information about them, that is their actions or their
utterances.” (Zukerman and Albrecht, 2001)
Semantic Analysis
“A technique using semantic-based tools and ontologies in
order to gain a deeper understanding of the information being
stored and manipulated in an existing system” (McComb, 2004)
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4. Problem Statement: Research Question
Web users generate a massive
unstructured information flow
?
Who has scientific information
relevant for me?
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5. Problem Statement: Use Case
Connecting researchers based on shared scientific events
(conferences)
Scientific Profiling
Scientific
User Model Event Model Conferences
Resource
Researchers
Profiler/
Analyzer
Researcher
(User)
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6. Social Semantic Web
Social Web Semantic Web
Community of (micro)blogging,
researchers with sharing,
conference tagging,
experience discussion
semi-structured
information
Larger population of system
people interested in
(faceted) search
scientific conferences
engine
recommendation clustered and
engine analyzed data
Human process Machine process
(Gruber, 2007)
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7. Social semantic Web
‣Hashtags as Identifiers
‣not always strong or consistent enough
‣properties of good hashtags formalized
‣helpful in assessment of valuable identifiers
(Laniado and Mika, 2007)
‣Expert Search/Profiling with Linked Data
‣aggregate and analyze certain types of data
‣need to surpass limits of closed data sets
‣LOD delivers multi-purpose data
(Stankovic et al., 2010)
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8. Scope & Value of the Study
‣Bridging research areas
Human Computer-Interaction & Semantic Analysis
‣Integration
Social network data and linked open data
‣Framework driven methodology
based upon current state-of-the-art semantic tools
‣Evaluation: improved connectivity
proof-of-concept Research 2.0 application
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9. Solution
‣Overview
‣Framework
‣Web Service
‣Client Application
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10. Solution: Overview
Annotate Data from Social Networks
Community approved
ontologies: FOAF, SIOC
Linked Open Data Applications
Scientific Profiling Framework
Connect People and Resources
that share Scientific Affinities
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11. Solution: Overview
Social Linked Open
Output Format
Networks Data Cloud
Framework Aggregate Interlink Publish
Archived/Cached Scientific
Linked Data Information
Data Annotate Analyse
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12. Solution: Overview
Social Linked Open
Output Format
Networks Data Cloud
Framework Aggregate Interlink Publish
Archived/Cached Scientific
Linked Data Information
Data Annotate Analyse
DBPedia JSON
Twitter Colinda RDF (XML)
GeoNames
Aggregate Interlink Publish
Semantic Scientific
Grabeeter Profiling API
Annotate Profiling Network Analyse
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13. Solution: Grabeeter
= Twitter aggregation & archiving tool
(developed at TUGraz)
http://grabeeter.tugraz.at
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14. Solution: Grabeeter
= Twitter aggregation & archiving tool
(developed at TUGraz)
http://grabeeter.tugraz.at
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15. Solution: Framework Architecture
Applications
Programming Interface
Analysis
High Level Queries
Extraction Interlinking
SQL Queries Triplification SPARQL Queries
Grabeeter RDF Store
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16. Solution: Web Service
‣get User Profile
‣find People or Events given a User Profile
‣register a new User Profile
‣get Event Details
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17. Solution: Web Service
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18. Solution: Web Service
16

19. Solution: Web Service
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20. Solution: Web Service
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21. Solution: Web Service
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22. Evaluation
‣Approach
‣Usability
‣Usefulness
‣Discussion
19

23. Evaluation: Approach
‣Test usability & usefulness
‣Web application: “Researcher Affinity Browser”
‣Using explicit evaluation questionnaire
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24. Evaluation: Usability
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25. Evaluation: Usefulness
‣Relevance
Test users rate their search results
‣Satisfaction questionnaire
Targeted questions about usefulness
Allow comments on user interface
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26. Evaluation: Usefulness
Relevant user percentage
Number of users
0% (None)
1-20% (A few)
21-40% (Less than one half)
41-60% (About one half)
61-80% (More than one half)
81-99% (Almost all)
100% (All)
0 1 2 3 4
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27. Evaluation: Usefulness Usefulness Questionnaire Results
Concept Affinity
Clear view of affinities between people
Map & Plot combination understood
Deactivating filer fast enough
Activating filer fast enough
Never usability glitches
Convention between views understood
Information display not overwhelming (confusing)
Relevant detailed person info
Shown details correspond with ‘real life’ activities
Enough relevant (new) persons
Daily updating of information obvious
Twitter data made more useful for researchers
1 2 3 4 5
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28. Evaluation: Discussion
‣ Affinities exposed in an engaging way
‣ Positive match according to users
Triggered by how many common entities?
After investigation of suggested users?
‣ Reliability of person details hard to verify
‣ UI satisfaction user dependent
‣ What does the user expect from “Affinity Browser”?
‣ Test different scenarios to identify usage types?
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29. Conclusion
‣ Framework supports social semantic-based applications
‣ Realized with current state-of-the-art technologies
‣ Interlinking with Linked Open Data Cloud enriches social network
data
‣ Researcher Affinity Browser
‣ Exposes affinities between users
‣ User feedback affirms positively new view on social data
‣ Hash tags identified as conferences provide consistent links
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30. Future work
‣ Rank tags
by importance, not just frequency of use
‣ Visualization
improve viewing of links between users and entities
‣ Multiple Resources
better reliability and more verification of data
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