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 proles 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 profiles and compare them to a reference profile.
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Aligning Web Collaboration Tools with Research Data for Scholars
1. Aligning Web Collaboration Tools
with Research Data for Scholars
Laurens De Vocht
Selver Softic, Erik Mannens, Martin Ebner, Rik Van de Walle
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. 1. Research Collaboration
2. Semantic Search Infrastructure
3. Dynamic Alignment of Resources
4. Evaluation
5. Conclusions
Agenda
4. 2. Semantic Search Infrastructure
3. Dynamic Alignment of Resources
4. Evaluation
5. Conclusions
1. Research Collaboration
17. @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
24. 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
25. 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
Notes de l'éditeur
- 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
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.