A Methodology for Extracting Knowledge about Controlled Vocabularies from Textual Data using FCA-Based Ontology Engineering

We introduce an end-to-end methodology (from text processing to querying a knowledge graph) for the sake of knowledge extraction from text corpora with a focus on a list of vocabularies of interest. We propose a pipeline that incorporates Natural Language Processing (NLP), Formal Concept Analysis (FCA), and Ontology Engineering techniques to build an ontology from textual data. We then extract the knowledge about controlled vocabularies by querying that knowledge graph, i.e., the engineered ontology. We demonstrate the significance of the proposed methodology by using it for knowledge extraction from a text corpus that consists of 800 news articles and reports about companies and products in the IT and pharmaceutical domain, where the focus is on a given list of 250 controlled vocabularies.