An information retrieval system retrieves a set of bibliographic citations in response to a user query. The user formulates a query by selecting Ireywords that express his/her information needs. In query formulation, different persons may use same terms to imply different meaning based on their background and experience. The system, however, is unable to perceive different user viewpoints of terms because the system is built to assign a unique meaning to each term. In this paper, we develop the technique for constructing user profiles to enable the system to learn individual user interpretation of keywords. The user profiles arc developed by elicitmg user opinion, as we] as vocabulary, by employing techniques from Personal Construct Theory. The elicited opinion is analysed through machine learning heuristics, This leads to a user profile that correlates user vocabulary to index terms in system representation of documents. The techniques are experimentally validated.