Attribute expansion relation extraction approach for smart engineering decision‐making in edge environments

Abstract

SummaryIn sedimentology, the integration of intelligent engineering decision‐making with edge computing environments aims to furnish engineers and decision‐makers with precise, real‐time insights into sediment‐related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision‐making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment‐related knowledge in the realm of intelligent engineering decision‐making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi‐source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute‐extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. After the relationship extraction, a proprietary sediment knowledge graph is constructed with the extracted triads.