This document discusses using linked open data to find experts who can solve problems from different domains. It presents challenges in open innovation on the semantic web, such as finding experts from various sources and related domains. Methods for expert finding before linked data are described. The document then proposes using linked data metrics based on topic distribution and proximity to select expert finding hypotheses. Finally, it describes a prototype expert finding system and the next challenge of exploring relevant knowledge domains to expand expert search.

1. Open Innovation and Semantic Web :
Problem Solver Search on Linked Data
Milan Stankovic
hypios & STIH – Université Paris-Sorbonne

2. 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

3. Expert Finding before Linked Data
Content User Activities Reputation and
Acheivements
user-generated content
publications, e-mails,
blogs, Wikipedia pages…
Buitelaar, P., &Eigner, T. (2008) ;;
Kolari, P., Finin, T., Lyons, K.,
&Yesha, Y. (2008) ….
content owned by users
Semantic desktop
Demartini, G., &Niederée, C.
(2008)
online activities
question answering,
bookmarking
Adamic et al. (2008) ; Zhang et al..
(2007) …
offline activities
obtaining research grants,
participating in projects
endorsment of user’s
content
Noll et al.(2009). ..
replies
Jurczyk, P., &Agichtein, E. (2007).
data structured
data
selection and
ranking of
experts

4. A hidden assumption: Experties
hypothesis
Expert
Candidate
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

5. Expert Search on Linked Data
selection and
ranking of
experts
expertise
hypothesis

6. 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

7. Linked Data Metrics
• Metrics based on topic distribution
• Metrics based on topic proximity

8. • 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

9. 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;
problem
topic 1
topic 2
Recommend
hypothesis
VoID + SCOVO
Find Experts
Invite
Experts
Recommend
Problems

10. Questions Please?
Milan Stankovic
milan.stankovic@hypios.com