Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis Articles uri icon

authors

  • VALVERDE ALBACETE, FRANCISCO JOSE
  • GONZÁLEZ CALABOZO, JOSE MARÍA
  • PEÑAS, ANSELMO
  • PELAEZ MORENO, CARMEN

publication date

  • February 2016

start page

  • 198

end page

  • 216

volume

  • 44

International Standard Serial Number (ISSN)

  • 0957-4174

Electronic International Standard Serial Number (EISSN)

  • 1873-6793

abstract

  • In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.

keywords

  • scientific knowledge discovery; exploratory data analysis; landscapes of knowledge; metaphor theory; formal concept analysis; k-formal concept analysis; extended formal concept analysis; semiring theory; confusion matrix; relation extraction; gene expression data; exploratory data-analysis; model