Managing polysemy and synonymy in science mapping using the mixtures of factor analyzers model

A new method for mapping the semantic structure of science is described. We assume that different researchers, working on the same set of research problems, will use the same words for concepts central to their research problems. Therefore, different research fields and disciplines should be identifiable by different words and the pattern of co-occurring words. In natural language, however, there is quite some diversity because many words have multiple meaning. In addition, the same meaning can be expressed by using different words. We argue that traditional factor analytic and cluster analytic techniques are inadequate for mapping the semantic structure if such polysemous and synonymous words are present. Instead, an alternative model, the mixtures of factor analyzers (MFA) model, is utilized. This model extends the traditional factor analytic model by allowing multiple centroids of the dataset. We argue that this model is structurally better suited to map the semantic structure of science. The model is illustrated by a case study of the uncertainty literature sampled from data from the ISI Web of Science. The MFA model is applied with the goal of discovering multiple, potentially incommensurate, conceptualizations of uncertainty in the literature. In this way, the MFA model can help in creating understanding of the use of language in science, which can benefit multidisciplinary research and interdisciplinary understanding, and assist in the development of multidisciplinary taxonomies of science.