LLMs on the Rise: Neuro-Symbolic AI for Knowledge Graph Construction in Manufacturing (Systematic Literature Review)

Numerous digitization activities in manufacturing led to an enormous increase in available, accessible data. Knowledge graphs (KGs) become increasingly popular in this domain as they show strengths in integrating different data sources and serve as a basis for downstream tasks. Yet, constructing a KG is still a challenging and time consuming process. Neuro-symbolic AI approaches, especially with powerful LLMs, have shown promising potentials in research and industry and can support KG construction. Nevertheless, KG construction with neural methods must be aware of, or ideally even handle, the inexplicability of results when applying the KG on manufacturing downstream tasks, e.g., on tasks of reliability- or safety-relevance. This makes it interesting to evaluate the utilization of neuro-symbolic AI and LLMs in KG construction in manufacturing. To the best of our knowledge, there is no systematic literature research on neuro-symbolic AI and LLMs in KGs in manufacturing, yet. Hence, this paper conducts a systematic literature review on neuro-symbolic AI and LLMs in KG construction in manufacturing. We show a solid increase of relevant publications on manufacturing KG construction and further show that BERT embeddings, RNN encodings, especially BiLSTM, CRF decodings, and, recently, LLMs, are common components of knowledge extraction from text documents to build KGs in manufacturing. With this systematic review we support both further research as well as industry application in this field. The main question to guide this review is "Which role play neuro-symbolic AI, especially LLM approaches in knowledge graph construction for manufacturing?".