Data-Driven Method of Knowledge Graph Construction for the Photovoltaic Industry Chain

Abstract

As the global demand for renewable energy increases, the photovoltaic (PV) industry, which is a vital component of clean energy, plays a crucial role in achieving energy transition and sustainable development goals. Consequently, the PV industry has grown rapidly in recent years, leading to an expanded and increasingly sophisticated industrial chain. However, the effective assessment of the development of the PV industry is complicated by the extensiveness of the PV industrial chain.

This paper presents a method for constructing a knowledge graph of the PV industry chain using enterprise bidding data, effectively coupling the product and supply networks. First, by leveraging relevant knowledge in the PV field, we employ two deep learning models, the BERT-BiLSTM-CRF model and an improved CasRel model, for entity and relationship extraction, respectively. Subsequently, entity-linking technology is applied to facilitate knowledge fusion. Finally, the Neo4j graph database is utilized for knowledge storage and graphical representation, comprehensively illustrating the technical process of constructing the PV industry chain knowledge graph.

The knowledge graph of the PV industry chain facilitates a timely understanding of the industry’s overall status and development trends while identifying bottlenecks and risks within the chain. Furthermore, it can aid enterprises in devising more effective risk management strategies and countermeasures, continuously optimizing the industry chain structure, and promoting the sustainable development of the industry.

Keywords

Photovoltaic industry chain / knowledge graph / BERT-BiLSTM-CRF / CasRel / enterprise bidding data

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Systems Engineering Society of China and Springer-Verlag GmbH Germany