1 / 13

ArnetMiner: Extraction and Mining of Academic Social Networks

ArnetMiner: Extraction and Mining of Academic Social Networks. Presenter : Cheng-Feng Weng Authors : Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, Zhong Su 2009/03/11. KDD.9 (2008). Outline. Motivation Objective Methods Experiments Conclusion Comments. Motivation(1/2).

anoki
Download Presentation

ArnetMiner: Extraction and Mining of Academic Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ArnetMiner: Extraction and Miningof Academic Social Networks Presenter : Cheng-Feng Weng Authors :Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, Zhong Su 2009/03/11 KDD.9 (2008)

  2. Outline • Motivation • Objective • Methods • Experiments • Conclusion • Comments

  3. Motivation(1/2) • People are not only interested in searching for different types of information (such as authors, conferences, and papers), but are also interested in finding semantics-based information(such as structured researcher profiles).

  4. Motivation(2/2) • Lack of semantics-based information. • Information is sometimes incomplete or inconsistent. • Lack of a unified approach to efficiently model the academic network. • Different types of information in the academic network were modeled individually, can’t be captured.

  5. Objective • It proposed the ArnetMiner system: • Extracting researcher profiles automatically from the Web. • Integrating the publication data into the network from existing digital libraries. • Modeling the entire academic network. • Providing search services.

  6. Researcher Profile Extraction a)separate the text into tokens b)assign possible tags to each token(CRF model) researcher name Google API SVM classifier SVM homepage/introducing page

  7. Name Disambiguation ‘David Mitchell’ and ‘Andrew Mark’ 2-CoAuthor relationship ‘David Mitchell’ and ‘Fernando Mulford’ Hidden Markov Random Fields (HMRF) framework …‘Andrew Mark’ and ‘Fernando Mulford’…

  8. Formalization using HMRF • The conditional distribution of the researcher: labels y given the observations x (papers) is diagonal matrix King papers {Xi, Xi+1,…Xn} King’ profile

  9. Modeling Academic Network • With a multinomial distribution • ACT Model 1: • determine topics first • ACT Model 2: • first choose a publication venue • ACT Model 3: • first write a paper

  10. Academic Search Services Association Search: Dijkstra algorithm

  11. Experiments

  12. Conclusions • It proposed a unified topic model to simultaneously model the different types of information in the academic network. The modeling results have been applied to expertise search and association search. • To deal with the name ambiguity

  13. Comments • Advantage • Consider many concepts • Is a complete application system • Drawback • Performance could be more improved • Application • Library

More Related