Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific subgraph patterns can be efficiently obtained by using relevant reasoning tools. Thus, we try to apply the knowledge graph to recognize linear building patterns. First, we use the property graph to express the spatial relations in proximity, similar and linear arrangement between buildings; secondly, the rules of linear pattern recognition are expressed as the rules of knowledge graph reasoning; finally, the linear building patterns are recognized by using the rule-based reasoning in the built knowledge graph. The experimental results on a dataset containing 1289 buildings show that the method in this paper can achieve the same precision and recall as the existing methods; meanwhile, the recognition efficiency is improved by 5.98 times.

翻译：建筑模式是反映城市物质与社会经济对区域影响的重要城市结构。以往研究多基于图同构方法并采用规则识别建筑模式，效率较低。知识图谱利用图结构建模实体间关系，通过相关推理工具可高效获取特定子图模式。因此，我们尝试将知识图谱应用于线性建筑模式识别。首先，利用属性图表示建筑之间邻近、相似及线性排列的空间关系；其次，将线性模式识别规则表达为知识图谱推理规则；最后，通过已构建知识图谱中的基于规则的推理实现线性建筑模式识别。在包含1289栋建筑的数据集上的实验结果表明：本文方法在精确率和召回率上与现有方法持平，同时识别效率提升了5.98倍。