Enhancing relevant concepts extraction for ontology learning using domain time relevance

Abstract

Concepts extraction is the backbone task for an ontology construction system. Identifying the relevant concepts is considered one of the main challenges of automatic ontology construction (called Ontology Learning (OL)). This research introduces a novel relevance metric that estimates the sustainability distribution of concepts to identify the modern and relevant concepts, called Domain Time Relevance (DTR). Also, this research proposes a Developed Concepts extraction Model based on the proposed DTR, namely DTR-DCEM. This proposed model uses the proposed Concepts extraction stopwords (CE-stopwords) list to avoid noise data. This proposed model, DTR-DCEM, aims to extract the relevant concepts from scientific publications. The experiments are conducted in two different datasets: DL2019 and ACLRelAcS. The experimental results show that the proposed model DTR-DCEM outperforms the state-of-the-art models. It got between (3.32∼51.07)% better performance than comparative models for the DL2019 dataset and between (3.29∼24.51)% better performance for the ACLRelAcS dataset. Moreover, the statistical significance of the proposed model DTR-DCEM has been proved using paired t-test.