Aligning Web Collaboration Tools with Research Data for Scholars

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Resources for research are not always easy to explore, and
rarely come with strong support for identifying, linking and
selecting those that can be of interest to scholars. In this
work we introduce a model that uses state-of-the-art semantic technologies to interlink structured research data and data from Web collaboration tools, social media and Linked Open Data. We use this model to build a platform that connects scholars, using their pro les as a starting point to explore novel and relevant content for their research. Scholars can easily adapt to evolving trends by synchronizing new social media accounts or collaboration tools and integrate then with new datasets. We evaluate our approach by a scenario of personalized exploration of research repositories where we analyze real world scholar pro files and compare them to a reference profi le.

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  • - Scholars -> collaboration + exploration- Resources for research are not always easy to explore, and- rarely come with strong support for identifying, linking and- selecting those that can be of interest to scholars.<> REVEAL- Linked Data : advantage : ideal to reveal links between resources and the type of relationship because of the specific graph structure of the data (RDF).- Because aligning multiple resources is a linking process, it makes sense to use a data structure that is designed for that purpose.- Semantic Web to be used both by humans and machines: -- entities used for interaction with user is represented as Linked Data for interaction with machines (software algorithm). -- Both have thus exact the same understanding of each concept (researcher, posts, publication).
  • Mention CollaborationTools + Social Media
  • Make sure the link between the user profiles and the Linked Open Data entities is explicitly mentioned and pointed (the above layer – below layer connection).Mention vocabularies last as formalization of the linking
  • Accuracy > SensitivitySensitivity = TP / (TP + FN) ->-> correctly recognized / ( falsely misses (should have been recognized) + correctly recognized)Accuracy= TP + TN / (TP + FP + TN + FN) ->-> ( correctly recognized + correctly missed ) / totalPrecision = TP / (TP + FP) -> ignoring unrecognized ->-> correctly recognized / total recognized
  • Golden profile is the reference for the other profiles and gets the highest score because it is optimized for our aligner module (DBLP and Mendeley and Twitter).
  • Accuracy low because many entities still unrecognized that should’ve been.
  • Sensitivity vs precision:Increasing precision improves sensitivity (fraction of correctly recognized)/(should have been recognized). UP1 slightly better, sensitivity – likely to the higher amount of publications in Mendeley library.
  • Mention following 0.04 vs 0.29 – 0.51 about the same as 0.42.UP1 – GP decrease : unrecognized authors/articles impact more negatively each the sensitivity (total = lower) – lack of proportional amount of DBLP publications.UP2 – GP strong increase : addition of Mendeley + DBLP0.53 higher than 0.29 because UP1 has less authors in comparison to the total number of users he follows.UP3 not applicable because no Mendeley profile was provided.
  • Aligning Web Collaboration Tools with Research Data for Scholars

    1. 1. Aligning Web Collaboration Tools with Research Data for Scholars Laurens De Vocht Selver Softic, Erik Mannens, Martin Ebner, Rik Van de Walle
    2. 2. Make the most of the wealth of resources for research: through relating and aligning scholar profiles with the online available resources, publications, conferences , and other scholar profiles.
    3. 3. 1. Research Collaboration 2. Semantic Search Infrastructure 3. Dynamic Alignment of Resources 4. Evaluation 5. Conclusions Agenda
    4. 4. 2. Semantic Search Infrastructure 3. Dynamic Alignment of Resources 4. Evaluation 5. Conclusions 1. Research Collaboration
    5. 5. Dynamic Alignment of Resources
    6. 6. 3. Dynamic Alignment of Resources 4. Evaluation 5. Conclusions 2. Semantic Search Infrastructure
    7. 7. 4. Evaluation 5. Conclusions 3. Dynamic Alignment of Resources
    8. 8. Extractor Aligner Profiler Interlinker
    9. 9. Extractor Aligner Profiler Interlinker
    10. 10. Extract Transform Map using selected vocabularies Load
    11. 11. Extractor Aligner Profiler Interlinker
    12. 12. http://www.resxplorer.org
    13. 13. Extractor Aligner Profiler Interlinker
    14. 14. @Alice is giving an interesting presentation at #ISWC2013 in #Sydney I presented in #Sydney our new demo, to be found at http://ceur-ws.org/Vol-1035… Linked Data Entities Explicit Connection Implicit Connection Tags @Selver @Laurens
    15. 15. 5. Conclusions 4. Evaluation
    16. 16. User Profiles Tags Articles and Authors
    17. 17. Golden Profile (GP) User Profile 1 (UP1) User Profile 2 (UP2) User Profile 3 (UP3) reference
    18. 18. Tags
    19. 19. Tags
    20. 20. Articles and Authors
    21. 21. Conference tags are better recognized (higher accuracy) Accuracy of the alignment between datasets crucial. Promising sensitivity when interlinking articles and authors. Even a low sensitivity guarantees useful links as each single correct link builds a novel connection of interest. Discussion
    22. 22. Aligned resources from scholars for exploring and searching reading library and contributions on web collaboration tools Future research will focus on Confirm findings using additional user profiles How to determine the efficiency of the model Improve the accuracy of the interlinking by post-processing the contributed resources with additional relations 5. Conclusions http://www.resxplorer.org @laurens_d_v #mmlab laurens.devocht@ugent.be http://slideshare.net/laurensdv http://semweb.mmlab.be/ Contact

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