Static and Dynamic Progressive Geospatial Interlinking

Abstract

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are insufficiently interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap through space tiling techniques, which significantly restrict the search space. Yet, the state-of-the-art techniques operate exclusively in a batch manner that produces results only after processing all their geometries. In this work, we address this issue by defining the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose a static progressive algorithm, which employs a fixed processing order, and a dynamic one, whose processing order is updated whenever new topological relations are discovered. We equip both algorithms with a series of weighting schemes and explain how they can be adapted to massive parallelization with Apache Spark. We conduct a thorough experimental study over six large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art. Special care is also taken to analyze the performance of the various weighting schemes.