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Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
Open Innovation and Semantic Web
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Open Innovation and Semantic Web

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presentation given at the Doctoral Consortium of International Semantic Web Conference (ISWC) 2010

presentation given at the Doctoral Consortium of International Semantic Web Conference (ISWC) 2010

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  • 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

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