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Venue Finder

Venue Finder. Outline. Goal Prototype Architecture Schedule Next to Do. Goal. Find top conferences and journals in a specific research area. Give a specific research area, then the system returns the relevance venues, including conferences and journals. Offline Part. Online Part. Index.

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Venue Finder

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  1. Venue Finder

  2. Outline • Goal • Prototype Architecture • Schedule • Next to Do

  3. Goal • Find top conferences and journals in a specific research area. • Give a specific research area, then the system returns the relevance venues, including conferences and journals.

  4. Offline Part Online Part Index Index Builder DBLP DB Ranking User Interface Query Impact FactorDB Impact Factor Builder Query Results from Scholar Google, Libra… Academic Search Tools Citation statistics from Scholar Google, Libra… Scholar Google, Libra… Prototype Architecture

  5. Impact Factor Builder • Build the impact factor of venues contained in DBLP DB. • Impact factor is estimated using the average citation rate. • Input: • All papers contained in DBLP DB. • Citation counts of individual papers are from Scholar Google and Libra. • Output: • Impact factors DB: Impact factors of venue. • Reference: http://citeseer.ist.psu.edu/impact.html

  6. Index Builder • Build the index based on paper titles and venue titles contained in DBLP DB. • Input: • All paper titles and venue titles contained in DBLP DB. • Output: • Index: the inverted index.

  7. Ranking • Rank the query result. • Search the matched venue titles and paper titles, and group them into several groups by venue title. • We can obtain several venue groups, including matched papers. • Calculate the ranking score using venue groups. • Input: • Query • Index, Impact DB and DBLP DB. • Academic search tools. • Output: • Ranking Result

  8. Ranking (cont.) • Ranking score. • Features • Term-matching score (T) • When the venue title includes query term, then term-matching score is higher. • 當一個venue中的所有paper, 其paper title含有query term的paper數量越多, 則term-matching score就越高. • Normalized impact score (N) • When the impact factor of venue is higher, then normalized impact score is higher. • Web score (W) • When the venue’s ranking order is top in academic search’s result, then web score is higher.

  9. Ranking (cont.)

  10. Ranking (cont.)

  11. Ranking (cont.)

  12. User Interface • Query process and result presentation. • Input: • Query. • Output: • Ranking result.

  13. Schedule • I’m going to do the following works this week • Build the impact factor DB. • In this week, I use Scholar Google to build DB first. • Build the index. • Build the ranking mechanism. • In this week, I use Libra to calculate the web score first. • Build the user interface. • Next to Do • Add different academic search tools into system. • I will also continue to build up a research area taxonomy. • It is helpful to analysis the relationship between venues and research areas.

  14. Next to Do • Automatic research area taxonomy generation. • Give a specific publication or research area, the system returns a set of publications that is relevant to this publication or research area. • Reference: • http://citeseer.ist.psu.edu/directory.html • Libra Computer Science Directoryhttp://libra.msra.cn/

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