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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

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Open Innovation and Semantic Web

  1. 1. Open Innovation and Semantic Web : Problem Solver Search on Linked Data Milan Stankovic hypios & STIH – Université Paris-Sorbonne
  2. 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. 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. 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. 5. Expert Search on Linked Data selection and ranking of experts expertise hypothesis
  6. 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. 7. Linked Data Metrics • Metrics based on topic distribution • Metrics based on topic proximity
  8. 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. 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. 10. Questions Please? Milan Stankovic