Geospatial data constitutes a considerable part of (Semantic) Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling, which determines the approach that reduces the search space, (ii) Budget-awareness, which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode, which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial's architecture and capabilities and perform thorough experiments to provide interesting insights about the relative performance of its algorithms.

翻译：地理空间数据构成(Semantic)网络数据的一大部分,但迄今为止,其来源在链接的开放数据云中并不完全相互关联。地理空间互连的目的是通过将地貌与地貌关系(例如Dimeal Exploitive 9-Intersection Model)的地形关系联系起来来填补这一差距。由于其四面形的时间复杂性,各种算法的目的是高效地进行地理空间互连。我们介绍了JedAI-spatial(JedAI-spatial)这个根据三个层面组织这些算法的新颖的开放源码系统:(一) 空间牵线,它决定了减少搜索空间的方法;(二) 预算意识,它区分了将算法与批量法和递进法的相互连接,以及(三) 执行模式,它分辨出序列算法,用单一的CPU-core和平行算法,在阿帕奇 Spark顶端运行。我们分析地描述了JedAI-spatial's 的架构和能力,并进行彻底的实验,以便了解其相对性能。