An Adaptive Knowledge Graph Construction Method for Semi-Structured Data

In the current transition of warehousing and logistics companies from traditional databases to knowledge bases, numerous challenges arise, including low efficiency due to massive data volumes and extended investment recovery periods due to high implicit costs. To address these issues and enable the rapid construction of a Knowledge Graph (KG) utilizing existing semi-structured data, this paper introduces an automated method for reading Excel content, identifying KG ontologies and entities, and importing data into Neo4j software. Firstly, the method is designed to automatically extract internal data from Excel, achieving the automatic recognition of KG ontologies and entity information. Secondly, this method is tailored to the KG triplet structure, enabling the automated transcription of these triplets into Neo4j software. Thirdly, to enhance the universal adaptability of the method, the functionality has been extended to enable arbitrary additions, reductions, and modifications to the content and data length of Excel spreadsheets. Finally, experiments have validated the method's effectiveness, universal applicability, and convenience. Moreover, this method is broadly applicable across various industries, offering an efficient approach for the automatic construction of KGs by business personnel.