Semantically Driven Social Data Aggregation Interfaces for Research 2.0

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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.

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Semantically Driven Social Data Aggregation Interfaces for Research 2.0

  1. 1. Semantically Driven Social DataAggregation Interfaces for Research 2.0 Laurens De Vocht Selver Softic Martin Ebner Herbert Mühlburger http://www.semanticprofiling.net September 7, 2011
  2. 2. Agenda ‣Problem Statement ‣Social Semantic Web ‣Solution ‣Evaluation ‣Conclusion 2
  3. 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) 3
  4. 4. Problem Statement: Research QuestionWeb users generate a massiveunstructured information flow ? Who has scientific information relevant for me? 4
  5. 5. Problem Statement: Use CaseConnecting researchers based on shared scientific events(conferences) Scientific Profiling Scientific User Model Event Model Conferences Resource Researchers Profiler/ Analyzer Researcher (User) 5
  6. 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) 6
  7. 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) 7
  8. 8. Scope & Value of the Study‣Bridging research areasHuman Computer-Interaction & Semantic Analysis‣IntegrationSocial network data and linked open data‣Framework driven methodologybased upon current state-of-the-art semantic tools‣Evaluation: improved connectivityproof-of-concept Research 2.0 application 8
  9. 9. Solution ‣Overview ‣Framework ‣Web Service ‣Client Application 9
  10. 10. Solution: OverviewAnnotate Data from Social Networks Community approved ontologies: FOAF, SIOC Linked Open Data Applications Scientific Profiling Framework Connect People and Resources that share Scientific Affinities 10
  11. 11. Solution: Overview Social Linked Open Output Format Networks Data Cloud Framework Aggregate Interlink Publish Archived/Cached Scientific Linked Data Information Data Annotate Analyse 11
  12. 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 11
  13. 13. Solution: Grabeeter= Twitter aggregation & archiving tool(developed at TUGraz)http://grabeeter.tugraz.at 12
  14. 14. Solution: Grabeeter= Twitter aggregation & archiving tool(developed at TUGraz)http://grabeeter.tugraz.at 12
  15. 15. Solution: Framework Architecture Applications Programming Interface Analysis High Level Queries Extraction Interlinking SQL Queries Triplification SPARQL Queries Grabeeter RDF Store 13
  16. 16. Solution: Web Service‣get User Profile‣find People or Events given a User Profile‣register a new User Profile‣get Event Details 14
  17. 17. Solution: Web Service 15
  18. 18. Solution: Web Service 16
  19. 19. Solution: Web Service 17
  20. 20. Solution: Web Service 18
  21. 21. Solution: Web Service 18
  22. 22. Evaluation ‣Approach ‣Usability ‣Usefulness ‣Discussion 19
  23. 23. Evaluation: Approach‣Test usability & usefulness‣Web application: “Researcher Affinity Browser”‣Using explicit evaluation questionnaire 20
  24. 24. Evaluation: Usability 21
  25. 25. Evaluation: Usefulness ‣Relevance Test users rate their search results ‣Satisfaction questionnaire Targeted questions about usefulness Allow comments on user interface 22
  26. 26. Evaluation: UsefulnessRelevant 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 23
  27. 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 understoodInformation display not overwhelming (confusing) Relevant detailed person infoShown 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 24
  28. 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? 25
  29. 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 26
  30. 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 27

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