Knowledge graph construction with meta-learning for continuously accumulated manufacturing knowledge

The construction of manufacturing knowledge graph (MKG) has been regarded as an important technical roadmap to support designer-oriented manufacturing knowledge reuse. It can improve product manufacturability and reduce design iterations. However, manufacturing knowledge is lesson-learned texts of enterprises. Traditional deep learning-driven MKG construction requires sufficient training samples, which heavily rely on manual labeling. It is both time-consuming and labor-intensive. Meanwhile, due to the new manufacturing knowledge accumulation, an MKG also needs to be continuously updated. To bridge the gap, this paper proposes an efficient MKG construction approach with meta-learning. Based on the manufacturing knowledge ontology, a novel two-stage knowledge extraction model (TKEM) is presented to achieve low-resource entity recognition. Then, considering the newly accumulated manufacturing knowledge, a continuous knowledge fusion strategy is illustrated to complete the MKG construction and update. Finally, the experimental results show that the TKEM outperforms state-of-the-art baselines on both the manufacturing knowledge dataset and a public dataset. In addition, a prototype system provides the application of MKG-based manufacturing knowledge reuse, which can perceive explicit and implicit knowledge requirements of designers by MKG embedding learning. • Proposing an efficient manufacturing knowledge graph construction approach. • A two-stage knowledge extraction model achieves low-resource entity recognition. • Continuous knowledge fusion facilitates manufacturing knowledge graph updates. • A prototype system demonstrates the effectiveness of the approach. • The approach outperforms the state-of-the-art baselines in entity recognition.