Open Innovation and Semantic Web :
Problem Solver Search on Linked Data
hypios & STIH – Université Paris-Sorbonne
Challanges for OI on Semantic Web
• Specifics of OI:
– we seek innovative and disruptive solutions, that
might come form many places not necesairly best
• Challanges for SW:
– find experts using existing Linked Data sources
– Find related domains where the solver might
Expert Finding before Linked Data
Content User Activities Reputation and
blogs, Wikipedia pages…
Buitelaar, P., &Eigner, T. (2008) ;;
Kolari, P., Finin, T., Lyons, K.,
&Yesha, Y. (2008) ….
content owned by users
Demartini, G., &Niederée, C.
Adamic et al. (2008) ; Zhang et al..
obtaining research grants,
participating in projects
endorsment of user’s
Noll et al.(2009). ..
Jurczyk, P., &Agichtein, E. (2007).
A hidden assumption: Experties
If the user
wrote a paper
saved a bookmark
saved a bookmark
before the others
then he/she is an
then he/she is a
Expert Search on Linked Data
How to Choose an Expertise Hypothesis
• Look at the structure of data:
– global data or local data store
– dataset caracteristics already published with VoID and
– Tools that index data summeries: Khatchadourian, S.,
& Consens, M. (2010); Harth et al. (2010).
• We propose Linked Data metrics based on:
– data quantity
– topic distribution
– topic proximity
Linked Data Metrics
• Metrics based on topic distribution
• Metrics based on topic proximity
• What has been done so far
– pilot study
• What’s been keeping us busy
– qualitative experiment: is there a correlation
between the values of the metrics and the
precsion and recall expectation of a hypothesis
Hypothesis Recommendation and
Expert Finding system
• Hy.SemEx system
• Next Challange: Provide a way to explore
relevant domains of knowledge and include
them in the expert search.
– considered work in: Recommender Systems based
on semantic proximity; Serendipity;
VoID + SCOVO
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