Functional requirements mining for mass personalisation: a heterogeneous data-driven knowledge graph approach to enable product rapid design

Under the paradigm of mass personalisation, enterprises are compelled to accurately and efficiently extract functional requirements (FRs) to respond to ever-changing customer requirements (CRs) and evolving technology-driven requirements (TDRs). However, traditional FRs mining methods suffer from high labour intensity, fragmented design knowledge, and insufficient integration of heterogeneous requirement data. To address these issues, an FRs mining approach based on a heterogeneous data-driven knowledge graph (HDDKG) is proposed, in which the CRs domain, TDRs domain and FRs domain are integrated into a unified multi-domain representation. Firstly, a quadruple-task CRs extraction and classification model is developed to capture both explicit and implicit CRs from online reviews, while a tailored relation extraction model is constructed to identify TDRs and their functional semantics from patent documents. Secondly, FR entities are extracted from product instructions. Subsequently, a Cosine Sentence model is employed to establish semantic mappings from CRs and TDRs to FRs. Finally, a case study on FRs mining is conducted to verify the effectiveness of the proposed HDDKG-based approach, and an extended exploration of the LLM-based multi-agent system provides a promising direction for future intelligent FRs mining.