The role of domain knowledge in cognitive modeling of information search

Abstract

Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the non-expert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.