Local Knowledge Mining of Architectural Heritage Semantic Fragments Based on Knowledge Graph Alignment

In the field of digital architectural heritage, the mining of tacit local knowledge embedded in architectural heritage is considered essential for the preservation, inheritance, and application of regional architectural characteristics. Local knowledge can be formally represented through semantic models, by which the automated mining of tacit information can be facilitated. However, due to the incomplete preservation of physical buildings and the fragmented nature of historical records, local knowledge is often represented as semantic fragments. Consequently, existing semantic models are still challenged in terms of knowledge integration and reasoning. In this study, a knowledge graph was developed for representing local knowledge, in which fragmented local semantics were aligned at both the ontological and entity levels. Subsequently, implicit local knowledge mining is achieved through meta-path centrality propagation combined with expert evaluation on a graph visualization platform. The method was applied to eight historical buildings in a case study. The knowledge graph quality assessment results indicate excellent ontology utilization and property utilization. The knowledge mining results demonstrate that graph-based expert evaluation successfully enables knowledge Feature Ranking and knowledge Extinction Warning.