This is a demo paper aiming to demonstrate GeoLOD main features. GeoLOD is a web catalog of Linked Open Cloud (LOD) spatial datasets and classes and a recommender for relevant datasets and classes for link discovery processes. GeoLOD catalog parses, maintains and generates metadata about datasets and classes provided by SPARQL endpoints that contain georeferenced point instances. It ofers text and map-based dataset search functionality and dataset descriptions in GeoVoID, a spatial dataset metadata template that extends VoID. GeoLOD recommender pre-computes and maintains, for all classes in the catalog, ranked lists of relevant classes for link discovery. In addition, the on-the-fly recommender allows users to define an uncatalogued SPARQL endpoint, a GeoJSON file or a Shapefile and get class recommendations in real time. Generated recommendations can be automatically exported in Silk and LIMES configuration files in order to be used for a link discovery task. GeoLOD also ofers a REST API for the automated execution of the above tasks.
LOD cloud expansion ofers more options to users, but at the same time, complicates the
discovery of data sources that meet user needs. While several tools address linked data search
and discovery problems [
The GeoLOD catalog parses the LOD cloud [
The GeoLOD recommender generates ranked lists of spatial datasets and classes that may
contain related instances with a given dataset or class, so as to be further examined in link
discovery processes for the establishment of owl:sameAs links or related links. The
recommendation algorithm, fully presented in [
GeoLOD is related with two categories of systems: (a) dataset catalogs and (b) dataset
recommenders for link discovery. Dataset catalogs provide single entry points for discovering
available linked-data datasets and the most prominent examples are arguably the LOD cloud
and the Datahub. Other data catalogs include the LODAtlas [
Dataset recommenders for link discovery automatically recommend triplesets (e.g., datasets or classes) that may contain related instances to a given tripleset in order to be used as input in a link discovery process. Although several methodologies have been proposed to address the problem, only few are implemented in tools and web applications, such as TRTML [10] and LODSynthesis [11]. The main diference between our work and the above is that their recommendation algorithms are based on information about existing links between datasets, while ours is based on the similarity of the spatial distribution of datasets and classes instances. GeoLOD novelties also include: (a) a pre-computed list of class recommendations for link discovery for each identified spatial class in the web of data, (b) on-the-fly class recommendation for new SPARQL endpoints and spatial datasets in GIS formats (e.g., ESRI shapefiles) and (c) the export of a recommended pair of classes to Silk and LIMES configuration files for direct use in a link discovery process.
Figure 1 depicts the overall architecture of the GeoLOD system, which consists of a backend and
a frontend module. At the backend, the Dataset Collector component parses the LOD cloud and
the Datahub catalogs to discover datasets provided through SPARQL endpoints. Specifically,
LOD cloud exposes its list of datasets and their metadata in a online JSON file and Datahub
using the CKAN API. The component extract basic dataset metadata, such as their title and
endpoint URL, and stores them to the GeoLOD database. Then, the Spatial Dataset Analyzer &
Metadata Generator component sends ASK SPARQL queries to identify spatial datasets, that is,
datasets that contain terms from well-known spatial vocabularies, such as W3C Basic Geo and
GeoSPARQL, for capturing the geographic position of instances. The component also sends
SELECT SPARQL queries to retrieve the list of spatial classes for each identified spatial dataset
and to generate additional metadata that capture the number of spatial instances, the bounding
box and the spatial vocabularies found in the datasets and stores them in the GeoLOD database.
The third backend component is the Class Recommender, which recommends relevant classes
for link discovery to a given class by first building “spatial” summaries of classes and then
computing their similarity [
The GeoLOD backend API was developed in Node.js and the frontend application in React. The GeoLOD database is a PostgreSQL with the PostGIS extension for spatial data management. GeoLOD is hosted in a Ubuntu 18 LTS 4GB Virtual Machine, provided by okeanos, a GRNET cloud Infrastructure as a Service (IaaS) for Greek academic institutes. GeoLOD content, that is, the list of spatial datasets and classes with their metadata and the recommendation lists for all classes, is updated automatically every two months, as a background process.
GeoLOD web application is available at http://geolod.net and is described in detail in [12]. In the GeoLOD frontend, users can browse the catalog of spatial datasets and classes and filter them using textual and spatial criteria. Upon entering a keyword in the Filters dialog box, GeoLOD searches in datasets and classes titles and descriptions, and upon selecting an area in the interactive map, GeoLOD returns datasets and classes whose bounding box intersect or are contained in the selected area. Upon selecting an item (a dataset or a class), users can view its full description and perform some actions. On a dataset description page (Fig. 2), users can view its title, description, SPARQL endpoint URL, its bounding box on a thumbnail, the spatial vocabularies it uses, the number of spatial instances and classes it contains, the number of recommendations and navigate in the list of dataset spatial classes. An icon indicates whether the SPARQL endpoint is currently available (green) or unavailable (red). In addition, users can download its VoID file (if available), its GeoVoID description and the dataset recommendations list in JSON. The latter can be used for as input in batch link discovery processes and consists of all recommendations for dataset classes. On a class description page, users can view its label, description, URI, the dataset it belongs to, its bounding box on a thumbnail, the spatial vocabulary it uses, the number of its spatial instances and the list of recommended classes and export the list of recommended classes in JSON. Furthermore, they can download live copies of class instances (extracted on the fly from the SPARQL endpoint) in RDF, JSON, and GeoJSON or browse class spatial instances on an interactive map. GeoJSON downloads are transformed in order to be readily consumable by a geographic information system (GIS) software, such as QGIS.
A snapshot of the recommendation list for a given class (e.g., for the Point class of the AEMET1 dataset that contains information about meteorological stations) is depicted in Fig. 3. Users can navigate through the list, view details for a recommended class, such as the number of estimated related instances and the ranking order, and export Silk and LIMES configuration ifles for the pair of classes for direct use in a link discovery process. The configuration files are automatically generated using as input the source (in this example Point) and the selected target class SPARQL endpoint URLs and URIs and configured to apply simple string and spatial rules for instance matching. GeoLOD also provides an on-the-fly recommender user interface for generating recommendations for datasets that are not listed in the GeoLOD catalog. Initially, users select the type of the input dataset that can be a SPARQL endpoint, a GeoJSON file, or a Shapefile. For the first case, they enter the URL of the endpoint and select a class from the automatically populated list; for the other cases, they upload the corresponding files. GeoLOD parses the input dataset, builds in real time the required metadata and summaries and searches in the catalog to return the list of recommended classes for link discovery.
GeoLOD also provides a REST API to serve its content in well-known templates and formats, enabling software-based consumption. Specifically, it provides services that expose catalog 1http://www.aemet.es/en/datos_abiertos content in DCAT [13] and JSON, dataset descriptions in GeoVoID and class link discovery recommendation lists for datasets and classes. The Table in Appendix lists the names, the request URI, and the descriptions of the main services.
GeoLOD has identified and currently lists 82 spatial datasets (out of 629 unique SPARQL endpoints URLs found in the LOD cloud and the Datahub) and 5,218 spatial classes. Regarding their geographic coverage, most datasets are “global”, that is, they contain instances all over the world, and most non-global datasets are located in and around Europe. GeoLOD provides approximately 90,000 class recommendations and on average it recommends 25 relevant classes per class. Regarding the performance of the recommendation algorithm executed in a 4GB RAM Ubuntu VM, it requires on average 18 minutes to generate the class recommendation list for each class (although the execution time depends on the source class size and spatial extent, ranging from a few seconds to several minutes), while the LIMES execution time for examining all possible pairs of spatial classes, that is, approximately 5,000, in order to generate instance links is approximately 5 hours. Possible lines of GeoLOD future development are the inclusion of datasets provided as RDF dumps, the further improvement of the recommendation algorithm in terms of eficiency and efectiveness, and the integration with Silk or LIMES web services for the instant generation of instance link matching.
/api/classes Class Descrip- /api/classes/<ID> tion Dataset GeoVoID /api/download/geovoid/<ID> Returns a DCAT-compliant turtle file that contains general information about GeoLOD and the list of the datasets in the Catalog Returns, in JSON, the list of datasets with their metadata (including internal dataset IDs) in the GeoLOD Catalog Returns, in JSON, the specified dataset metadata with the list of its classes. The dataset ID is a variable corresponding to the internal dataset ID. (e.g., http:// snf-661343.vm.okeanos.grnet.gr/api/datasets/915 returns the metadata for the AEMET dataset) Returns, in JSON, the list of classes with their metadata (including internal classes IDs) in the GeoLOD Catalog.
Returns, in JSON, the specified class metadata with the list of its recommended classes. The class ID is a variable corresponding to the internal class ID. (e.g., http://snf-661343.vm.okeanos.grnet.gr/api/ classes/139090 returns the metadata for the CaveEntrance class of Linklion dataset).
Returns, in turtle format, the GeoVoID description of the specified dataset.
Dataset Recom- /api/download/ datasemendations trecommendations/<ID> Returns, in JSON, the list of recommendations for all specified dataset classes.
Class Recom- api/download/ classesrecmenations ommendations/<ID>