Modeling individual cognitive structure in contextual information retrieval

  • Authors:

  • Xuan Tian;Xiaoyong Du;He Hu;Haihua Li

  • Affiliations:

  • School of Information Science and Technology, Beijing Forestry University, PR China and Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing 1008 ...;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing 100872, PR China and School of Information, Renmin University of China, Beijing 100872, PR C ...;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing 100872, PR China and School of Information, Renmin University of China, Beijing 100872, PR C ...;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing 100872, PR China and School of Information, Renmin University of China, Beijing 100872, PR C ...

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2009

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Abstract

In contextual information retrieval (CIR), the retrieval of information depends on the time and place of the submitting query, history of interaction, task in hand, and many other factors that are not given explicitly, but lie implicitly in the interaction and surroundings of searching, namely the context [P. Ingwersen, N. Belkin, Information retrieval in context, ACM SIGIR Forum 2 (2004)]. A user's individual cognition is one of important contextual factors to help understand his or her personal needs. In this paper, we give a formal definition for a user's individual cognitive structure (ICS) in CIR, and propose an approach called DOSAM to model it. DOSAM is inspired by the spreading activation model of psychology, and built on the domain ontology, while its goal is to get a user's cognitive structure. Cost analysis of construction algorithm shows that it is feasible to get ICS by DOSAM, and personalized search experimental results on a digital library indicate that ICS based search can improve the search effectiveness and a user's satisfaction.