A semantic approach for extracting domain taxonomies from text

• We present a semantic approach for learning domain taxonomies from text.
• Word sense disambiguation is applied on text and on existing taxonomies.
• We refine the subsumption method for term relations to include concept semantics.
• We define new semantic measures for evaluating the built taxonomies.
• Our method performs well for capturing the broader–narrower inter-concept relation.

In this paper we present a framework for the automatic building of a domain taxonomy from text corpora, called Automatic Taxonomy Construction from Text (ATCT). This framework comprises four steps. First, terms are extracted from a corpus of documents. From these extracted terms the ones that are most relevant for a specific domain are selected using a filtering approach in the second step. Third, the selected terms are disambiguated by means of a word sense disambiguation technique and concepts are generated. In the final step, the broader–narrower relations between concepts are determined using a subsumption technique that makes use of concept co-occurrences in a text. For evaluation, we assess the performance of the ATCT framework using the semantic precision, semantic recall, and the taxonomic F-measure that take into account the concept semantics. The proposed framework is evaluated in the field of economics and management as well as the medical domain.