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.

Entity Resolution is the task of identifying pairs of entity profiles that represent the same real-world object. To avoid checking a quadratic number of entity pairs, various filtering techniques have been proposed that fall into two main categories: (i) blocking workflows group together entity profiles with identical or similar signatures, and (ii) nearest-neighbor methods convert all entity profiles into vectors and identify the closest ones to every query entity. Unfortunately, the main techniques from these two categories have rarely been compared in the literature and, thus, their relative performance is unknown. We perform the first systematic experimental study that investigates the relative performance of the main representatives per category over numerous established datasets. Comparing techniques from different categories turns out to be a non-trivial task due to the various configuration parameters that are hard to fine-tune, but have a significant impact on performance. We consider a plethora of parameter configurations, optimizing each technique with respect to recall and precision targets. Both schema-agnostic and schema-based settings are evaluated. The experimental results provide novel insights into the effectiveness, the time efficiency and the scalability of the considered techniques.

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are inadequately interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking covers this gap with batch techniques that are restricted to individual topological relations, even though most operations are common for all main relations. In this work, we introduce a batch algorithm that simultaneously computes all topological relations and define 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 two progressive algorithms and conduct a thorough experimental study over large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art.

We extend YAGO2 with geospatial information represented by geometries (e.g., lines, polygons, multipolygons, etc.) encoded by Open Geospatial Consortium standards. The new geospatial information comes from official sources such as the administrative divisions of countries but also from volunteered open data of OpenStreetMap. The resulting knowledge graph is currently the richest in terms of geospatial information publicly available, open source, knowledge graph.

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of Linked Data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the Linked Data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.