OLAF: An Ontology Learning Applied Framework

Abstract

Since the beginning of the century, research on ontology learning has gained popularity. Automatically extracting and structuring knowledge relevant to a domain of interest from unstructured textual data is a major scientific challenge. After studying the main existing methods, such as Text2Onto, we propose a new approach with a modular ontology learning framework focusing on automatically extracting knowledge from raw text sources. We consider tasks from data pre-processing to axiom extraction. Whereas previous contributions considered ontology learning systems as tools to help the domain expert craft a reusable ontology, we developed the proposed framework with full automation in mind to build a minimum viable ontology targeted at an application. Ontology Learning Applied Framework (OLAF) has been generically designed to build specific ontologies whatever the application domain, use case and text data. We implement an initial version and test the framework on an ontology-based system, a search engine for technical products.