CONCEPTUAL MAPPING OF A DATABASE IN THE HUMANITIES: FIRST RESULTS OF AN EXPERIMENT WITH SOPHIA

Abstract

The idea of conceptual mapping goes back to the semantic differential and conceptual clustering. Using multivariate statistical techniques, one can map a dispersion of texts onto another dispersion of their content indicators, such as keywords. The resulting configurations of texts/indicators differ from one another according to their meaning, expressed in terms of co‐ordinates of a semantic field. We suggest that by using principal component analysis, one can design a user‐friendly semantic space which can be navigated. Further, to learn the names of embedded magnitudes in semantic space, the idea of conceptual clustering is used in a broader context. This is a two‐mode statistical approach, grouping both documents and their index terms at the same time. By observing the agglomerations of narrower, related terms over a corpus, one arrives at broader, more general thesaurus entries which denote and conceptualise the major dimensions of semantic space.

Citation

DARÁNYI, S., ZAWIASA, R. and HAJNAL, Z. (1996), "CONCEPTUAL MAPPING OF A DATABASE IN THE HUMANITIES: FIRST RESULTS OF AN EXPERIMENT WITH SOPHIA", Journal of Documentation, Vol. 52 No. 1, pp. 86-99. https://doi.org/10.1108/eb026963