@article{878,
  author = {Kamel Garrouch1, Mohamed Nazih Omri, Amira Kouzana},
  title = {A New Information Retrieval Model Based on Possibilistic Bayesian Networks},
  journal = {Journal of Information & Systems Management},
  year = {2012},
  volume = {2},
  number = {2},
  doi = {},
  url = {http://www.dline.info/jism/fulltext/v2n2/4.pdf},
  abstract = {This paper proposes a new Information Retrieval Model based on possibilistic Bayesian network. This model encodes the most important dependence relationships existing between terms. It focuses on local dependencies between terms within each document. The relevance of a document to a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. These two measures are also used for quantifying termsterms links and terms-documents links. Experiments carried out on three standard collections have proven the efficiency of the proposed model.},
}