  In this work, we propose a new approach for discovering various relationships
among keywords over the scientific publications based on a Markov Chain model.
It is an important problem since keywords are the basic elements for
representing abstract objects such as documents, user profiles, topics and many
things else. Our model is very effective since it combines four important
factors in scientific publications: content, publicity, impact and randomness.
Particularly, a recommendation system (called SciRecSys) has been presented to
support users to efficiently find out relevant articles.
