A Concept Semantic Similarity Method for Underground Pipeline Spatial Data Matching

Considering the shortcomings in the current methods for spatial data matching between urban underground integrated and professional pipelines, such as insufficient semantic expression, rough calculation method for semantic similarity and low matching quality, this study proposed a novel conceptual semantic similarity method that takes account of pipeline spatial feature. Firstly, the conceptual semantics were expressed by listing the pipeline conceptual attributes, and the entity spatial association of the pipeline was determined by entity topological characteristics. The pipeline ontologies were built with other characteristics. Secondly, a matching model was proposed based on the pipeline conceptual semantics, which takes advantages of matching metrics including conceptual attributes, spatial features and ontology hierarchical structure. More importantly, the matching entity in the model is determined by calculating the similarity degree of the pipeline entity with the weight information. The experimental results showed that this method can reasonably calculate the similarity of pipeline entities, and significantly improve the quality of pipeline spatial data matching.