With the proliferation of the Semantic Web technologies, more and more spatial knowledge bases are being published on the Web. Discovering spatial links among spatial knowledge bases is crucial in achieving real-time applications such as reasoning and question answering over spatial linked data. However, existing approaches rely on numerous high-cost Dimensionally Extended Nine-Intersection Model (DE-9IM) computations which lead to inefficient spatial link discovery. To address this problem, we propose a novel approach for discovering topological relations based on the spatial link composition, namely DORIC. Different from conventional spatial link discovery methods, DORIC further reduces the required number of DE-9IM computations by composing existing spatial links. Specifically, we first propose a spatial link composition (SLC) model to infer new spatial links of topological relations from existing or intermediate links. We replace part of high-cost DE-9IM computations with relatively low-cost SLC, and it leads to reduced spatial link discovery time. Then to maximize the utility of SLC during the process of DORIC, we design two effective strategies for deciding the discovery and access orders. Experiments on three real-world datasets show that the proposed DORIC outperforms the state-of-the-art approaches in terms of the spatial link discovery time.

随着语义网技术的普及，越来越多的空间知识库被发布到网上。发现空间知识库之间的空间链接对于实现空间链接数据的推理和问答等实时应用至关重要。然而，现有方法依赖于大量高成本的维度扩展九交模型 (DE-9IM) 计算，这导致空间链接发现效率低下。为了解决这个问题，我们提出了一种基于空间链接组合发现拓扑关系的新方法，即 DORIC。与传统的空间链路发现方法不同，DORIC 通过组合现有的空间链路进一步减少了 DE-9IM 计算所需的数量。具体来说，我们首先提出了一个空间链接组合（SLC）模型，以从现有或中间链接推断拓扑关系的新空间链接。我们用相对低成本的 SLC 替换了部分高成本的 DE-9IM 计算，从而减少了空间链路发现时间。然后为了在 DORIC 过程中最大化 SLC 的效用，我们设计了两种有效的策略来决定发现和访问顺序。在三个真实世界数据集上的实验表明，所提出的 DORIC 在空间链接发现时间方面优于最先进的方法。我们设计了两种有效的策略来决定发现和访问顺序。在三个真实世界数据集上的实验表明，所提出的 DORIC 在空间链接发现时间方面优于最先进的方法。我们设计了两种有效的策略来决定发现和访问顺序。在三个真实世界数据集上的实验表明，所提出的 DORIC 在空间链接发现时间方面优于最先进的方法。