Open Science Research Excellence
@article{(International Science Index):,
  title    = {Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval},
  author    = {Hager Kammoun and  Jean Charles Lamirel and  Mohamed Ben Ahmed},
  country   = {},
  abstract  = {In this paper, a model for an information retrieval
system is proposed which takes into account that knowledge about
documents and information need of users are dynamic. Two
methods are combined, one qualitative or symbolic and the other
quantitative or numeric, which are deemed suitable for many
clustering contexts, data analysis, concept exploring and
knowledge discovery. These two methods may be classified as
inductive learning techniques. In this model, they are introduced to
build “long term" knowledge about past queries and concepts in a
collection of documents. The “long term" knowledge can guide
and assist the user to formulate an initial query and can be
exploited in the process of retrieving relevant information. The
different kinds of knowledge are organized in different points of
view. This may be considered an enrichment of the exploration
level which is coherent with the concept of document/query
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {1},
  number    = {11},
  year      = {2007},
  pages     = {3516 - 3520},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 11, 2007},