Ontology Learning from Text Using Relational Concept Analysis

We propose an approach for semi-automated construction of ontologies from text whose core component is a relational concept analysis (RCA) framework which extends formal concept analysis (FCA), a lattice-theory paradigm for discovering abstractions within objects x attributes tables, to the processing of several sorts of individuals described both by own properties and inter-individual links. As a pre-processing, text analysis is used to transform a document collection into a set of data tables, or contexts, and inter-context relations. RCA then turns these into a set of concept lattices with inter-related concepts. A core ontology is derived from the lattices in a semi-automated manner, by translating relevant lattice elements into ontological concepts and relations, i.e., either taxonomic or transversal ones. The ontology is further refined by abstracting new transversal relations from the initially identified ones using RCA. We discuss as well the results of an application of the method to astronomy texts.