Using terms and informal definitions to classify domain entities into top-level ontology concepts: An approach based on language models

Abstract

The classification of domain entities into top-level ontology concepts remains an activity performed manually by an ontology engineer. Although some works focus on automating this task by applying machine-learning approaches using textual sentences as input, they require the existence of the domain entities in external knowledge resources, such as pre-trained embedding models. In this context, this work proposes an approach that combines the term representing the domain entity and its informal definition into a single text sentence without requiring external knowledge resources. Thus, we use this sentence as the input of a deep neural network that contains a language model as a layer. Also, we present a methodology used to extract two novel datasets from the OntoWordNet ontology based on Dolce-Lite and Dolce-Lite-Plus top-level ontologies. Our experiments show that by using the transformer-based language models, we achieve promising results in classifying domain entities into 82 top-level ontology concepts, with 94% regarding micro F1-score.