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presentation given at the Doctoral Consortium of International Semantic Web Conference (ISWC) 2010
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Open Innovation and Semantic Web : Problem Solver Search on Linked Data
Milan Stankovichypios & 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 experts
• Challanges for SW:– find experts using existing Linked Data sources– Find related domains where the solver might
come from
Expert Finding before Linked Data
Content User Activities Reputation and Acheivements
user-generated contentpublications, e-mails, blogs, Wikipedia pages…
Buitelaar, P., &Eigner, T. (2008) ;; Kolari, P., Finin, T., Lyons, K., &Yesha, Y. (2008) ….content owned by usersSemantic desktop
Demartini, G., &Niederée, C. (2008)
online activitiesquestion answering, bookmarking
Adamic et al. (2008) ; Zhang et al.. (2007) …offline activitiesobtaining research grants, participating in projects
endorsment of user’s content
Noll et al.(2009). ..
replies
Jurczyk, P., &Agichtein, E. (2007).
datadata structured data
structured data
selection and ranking of
experts
selection and ranking of
experts
A hidden assumption: Experties hypothesis
Expert Candidat
e
Expertise Evidence
Expertise Topic
hypothesis
If the user
wrote a paper
saved a bookmark
saved a bookmark before the others
was retweeted
on TopicX
then he/she is an expert
then he/she is a better ranked
expert
on TopicX
Expert Search on Linked Data
selection and ranking of
experts
selection and ranking of
experts
expertise hypothesisexpertise
hypothesis
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
SCOVO– 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
€
THt,s =Qt ,s
Qowl :topTraceClass,s
€
SHt,s =Qt ,sQt
€
avgPC =
1 dist(s1,s2)s2∈C
∑s1∈C
∑
n2
€
maxDC = maxs1 ,s2∈C
dist(s1,s2)
• 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;
problemproblemtopic 1topic 2
Recommend hypothesis
Recommend hypothesis
VoID + SCOVO
Find ExpertsFind Experts
Invite ExpertsInvite
Experts
Recommend Problems
Recommend Problems
