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Expert Finding in Bibliographic Network

Hashemi, Hadi | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45004 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Beigy, Hamid
  7. Abstract:
  8. Expert finding in bibliographic networks has received increasing attention in recent years. This task concerns with finding relevant researchers for a given topic. In this thesis, we propose a model to determine authority of authors who have participated in the Communities. This model has a little improvement over community based baseline model. However, due to the low performance of community based models, the proposed authority based model cannot improve the document based baseline models either. Therefore, we try to improve document based models, instead of community based models and have proposed two other models which are based on authors’ topic dominance for expert finding. Document based models that are used in bibliographic networks, simply assign equal expertise contributions to the authors of a document. Motivated by the observation that rarely do all coauthors contribute to a paper equally and large fraction of documents are written by more than one author, in this paper, we propose two discriminative methods to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. According to some observations, we recognize three feature groups that can discriminate relevant and irrelevant experts. Experimental results on a real dataset, and an automated generated one that is gathered from Microsoft academic search show that the proposed models significantly improve the performance of expert finding in terms of all common Information Retrieval evaluation metrics
  9. Keywords:
  10. Expert Finding ; Learning to Rank ; Discriminative Model

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