Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models
This work offers an incremental improvement in knowledge graph construction for domain-specific applications, particularly in automotive electrical systems.
This paper proposes a knowledge graph construction (KGC) approach to provide structured information for technology intelligence and cyber-physical systems planning. The method significantly outperforms GraphGPT, bi-LSTM, and transformer REBEL in class recognition, relationship construction, and 'subclass of' categorization on a pre-defined dataset.
In this paper we propose a novel approach based on knowledge graphs to provide timely access to structured information, to enable actionable technology intelligence, and improve cyber-physical systems planning. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. Following this data exploration process, we employ a selective knowledge graph construction (KGC) approach supported by an electronics and innovation ontology-backed pipeline for multi-objective decision-making with a focus on cyber-physical systems. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable. Our results demonstrate that our construction process outperforms GraphGPT as well as our bi-LSTM and transformer REBEL with a pre-defined dataset by several times in terms of class recognition, relationship construction and correct "sublass of" categorization. Additionally, we outline reasoning applications and provide a comparison with Wikidata to show the differences and advantages of the approach.