Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction

Abstract

Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain.

Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

“…Specifically, entity extraction has been developed for a long time, which involves statistical algorithms and machine learning algorithms, such as support vector machines [27,28], Conditional Random Fields [29,30], BiLSTM-CRF [31], large-scale pre-training models BERT [32]. Relation extraction is the next step after entity extraction and can be challenging when there are overlapping entities and relations [33]. Relation extraction initially utilises semantic rules and templates before machine learning algorithms such as BiLSTM [34] and Lattice LSTM [35].…”