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Over the past several years, the Web of Linked Data has continued to grow in size, both in terms of the breadth of domains covered as well as the depth and precision of knowledge. As a consequence to this growth, the community has been led to confront challenges that arise from incorporating large-scale geographic information into knowledge graphs. These challenges include data quality, data storage, data transmission, and the scaling of geospatial query processing. A crucial concern in real-time computing is about striking a balance between the time complexity of algorithms and memory consumption or data storage (i.e., space). Given a computational problem and the domain of its inputs, there are several decisions that researchers, engineers, and practitioners must make based on the constraints of available computational resources, as well as the desired program's `reaction' time for the sake of human-computer interaction. Understanding how to strike such a balance requires a thorough understanding of the data structures and algorithms used to solve a problem. Geospatial data and geospatial queries in particular require innovators to possess deep background knowledge in order to research and develop viable solutions. As a geographic information scientist working with Linked Data, I attempt to improve the quality, accessibility, reliability, and query performance of geographic data in knowledge graphs. In this dissertation, I study three specific trade-offs: (i) whether certain geographic properties and relations should be computed on-demand or materialized beforehand; (ii) whether carefully precomputing topological relations is more useful than providing users with geometries to compute topological relations on-demand; and finally, (iii) whether the challenges of hosting public geographic knowledge graph services on the Web can be mitigated, and at what cost, by a peer-to-peer architecture in which the clients possess more intelligence.

Geospatial extensions of SPARQL, like GeoSPARQL and stSPARQL, have been defined since 2007 and while several geospatial RDF stores have implemented a substantial part of these extensions, other stores limited their support mostly on point geometry features. A parallel process with the above was that RDF frameworks evolved in an interesting way by presenting a more mature set of geospatial features, such as GeoSPARQL support and including the latest indexing technologies. As a logical consequence, a shift in the use of RDF frameworks is to be expected, from base platforms that users extend to create more complete geospatial RDF stores, to attractive finished RDF solutions for many geospatial applications. Alongside with the ever-increasing size of linked geospatial data that semantic stores need to handle, all the above provided our group the motivation to improve our single node systems benchmark Geographica, originally defined in 2013. Geographica 2 is more comprehensive, because it now includes new geospatial RDF stores and frameworks, big real world datasets of many hundred million triples with up to fifty million features of complex geometries, new tests and queries that reveal the scalability of these systems. The augmented and revised real world workload of Geographica 2 tests the efficiency of primitive spatial functions in RDF stores, their performance in the geocoding scenario against the new Census dataset in addition to many other real use case scenarios and finally includes computation of statistics for geospatial datasets. A more detailed and systematic evaluation is performed using the synthetic workload. The new scalability workload aims at discovering the limits of centralized geospatial RDF stores of various architectures. It employs a set of six well balanced real world datasets with highly complex geometries covering many European countries and compares three RDF stores in terms of storage space, bulk loading and query response time. In addition, a special version of the benchmark has been created for systems with limited geospatial functionality and two more systems of this category are introduced along the six systems of the main benchmark, all stressed against point-only subsets of the workloads. Three out of the eight systems use an RDBMS for the persistence layer, while some of them offer a variety of persistence options., EU project ExtremeEarth (825258)

In 2012 the Open Geospatial Consortium published GeoSPARQL defining “an RDF/OWL ontology for [spatial] information”, “SPARQL extension functions” for performing spatial operations on RDF data and “RIF rules” defining entailments to be drawn from graph pattern matching. In the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic Web standard, as judged by references to it in other Semantic Web standards and its wide use for Semantic Web data. An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a charter to: handle outstanding change requests and source new ones from the user community and to “better present” the standard, that is to better link all the standard’s parts and better document & exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact presentation. In this paper, we describe motivating change requests and actual resultant updates in the candidate version 1.1 of the standard alongside reference implementations and usage examples. We also describe the theory behind particular updates, initial implementations of many parts of the standard, and our expectations for GeoSPARQL 1.1’s use.

To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

Linked Data is available data on the web in a standard format that is useful for content inspection and insights deriving from data through semantic queries. Querying and Exploring spatial and temporal features of various data sources will be facilitated by using Linked Data. In this paper, an application is presented for linking transport data on the web. Data from Google Maps API and OpenStreetMap linked and published on the web. Spatio-Temporal queries were executed over linked transport data and resulted in network and traffic information in accordance with the user’s position. The client-side of this application contains a web and a mobile application which presents a user interface to access network and traffic information according to the user’s position. The results of the experiment show that by using the intrinsic potential of Linked Data we have tackled the challenges of using heterogeneous data sources and have provided desirable information that could be used for discovering new patterns. The mobile GIS application enables assessing the profits of mentioned technologies through an easy and user-friendly way.

The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial semantic relations like crosses, nearby, etc., with all other city objects. This is a novel and original work practically implemented generic product for any 3D CityGML model on the globe. A user-friendly form-based interface is designed to compose effective ontology based GeoSPARQL query. CSSWC enhances CityGML applications performance through effective and efficient querying system.

We present a general encoding scheme for the efficient management of spatial RDF data. The scheme approximates the geometries of the RDF entities inside their (integer) IDs and can be used, along with several operators and optimizations we introduce, to accelerate queries with spatial predicates and to re-encode entities dynamically in case of updates. We implement our ideas in SRX, a system built on top of the popular RDF-3X system. SRX extends RDF-3X with support for three types of spatial queries: range selections (e.g., find entities within a given polygon), spatial joins (e.g., find pairs of entities whose locations are close to each other), and spatial k-nearest neighbors (e.g., find the three closest entities from a given location). We evaluate SRX on spatial queries and updates with real RDF data, and we also compare its performance with the latest versions of three popular RDF stores. The results show SRX ’s superior performance over the competitors; compared to RDF-3X, SRX improves its performance for queries with spatial predicates while incurring little overhead during updates.

Geometric data plays a central role in the geospatial domain, architectural design and construction industry. For upcoming, new approaches to store building data, such as the Semantic Web, no universal common agreement exists on the combination of geometric and non-geometric data. It can therefore be unclear to users on how to represent their geometries, leading to a decelerated application and advancement of making building data available over the web. This gap can only be bridged if a common approach on the representation of geometries on the web is achieved. To first generate a common understanding of geometry representations, an overview of existing and developing geometry (web) standards needs to be given and discussed, i.e., the Industry Foundation Classes (IFC), CityGML, GeoSPARQL, and the OntoBREP and GEOM ontologies. This discussion needs to consider general contexts, e.g., 2D, 3D, detailed, or tessellated geometries, and specific use cases of the construction industry. Based on these discussions, this paper aims to propose a general recommendation for web-based geometry representations to enhance future applications of building data on the web. Due to the variety of use cases and their requirements, as well as technical constraints based on deviant interpretations of geometry descriptions from different geometry kernels, it became clear, that no approach or standard is generally superior to others. The biggest distinction identified in this paper is posed between the context of visualizing, where simplified, tessellated geometry holds the highest advantage, and (parametric) modeling, which requires semantically detailed geometry representations. Hence, we recommend to interlink non-geometric data with multiple geometry representations, to address all relevant contexts and use cases appropriately. The individual geometry representations should be chosen based on the relevant use cases for an optimal experience when using and exchanging geometry on the web. With this recommendation, the benefits of all discussed approaches can be exploited while avoiding their respective challenges.

Geospatial data sources are heterogeneous and backed by different data management technologies. This brings problems in data integration as well as their subsequent interpretation. This article proposes a technique for choosing the relevant data source out of many such sources, given a complex spatial query. Each source is described with a set of prototypical queries that are algorithmically arranged into a lattice. Upon query execution, the lattice is searched for an element matching best the input query. The matching algorithm makes use of the GeoSPARQL query containment enhanced with OWL 2 QL semantics. The technique is implemented in a prototypical system called OnGIS.

Building Information Modelling (BIM) is a key enabler to support integration of building data within the buildings life cycle (BLC) and is an important aspect to support a wide range of use cases, related to intelligent automation, navigation, energy efficiency, sustainability and so forth. Open building data faces several challenges related to standardization, data interdependency, data access, and security. In addition to these technical challenges, there remains the barrier among BIM developers who wish to protect their intellectual property, as full 3D BIM development requires expertise and effort. This means that there is often limited availability of building data. However, a Linked Data approach to BIM, combined with a supporting national geospatial identifier infrastructure makes interlinking and controlled sharing of BIM models possible. In Ireland, the Ordnance Survey Ireland (OSi) maintains a substantial data set, called Prime2, which includes not only building GIS data (polygon footprint, geodetic coordinate), but also additional building specific data (e.g. form, function and status). The data set also includes change information, recording when changes took place and who captured and validated those changes. This paper presents the development of a national geospatial identifier infrastructure based on an OSi building ontology that supports capturing OSi building data using Resource Description Framework (RDF). The paper details the different steps required to generate the ontology and publish the data. First, an initial analysis of the data set to generate the ontology is discussed. This includes identification of mappings to existing standards, e.g. GeoSPARQL to handle geometries and PROV-O to handle provenance, to the development of R2RML mappings to generate the RDF and the method for deploying the ontology and the building graphs. This data is then made available dependent on different licensing agreements handled by an access control approach. Methods are then presented to support the interlinking of the authoritative data with other building data standards and data sets using geolocation, followed finally by discussion and future work.

With an increase in Geospatial Linked Open Data being adopted and published over the web, there is a need to develop intuitive interfaces and systems for seamless and efficient exploratory analysis of such rich heterogeneous multi-modal datasets. This work is geared towards improving the exploration process of Earth Observation (EO) Linked Data by developing a natural language interface to facilitate querying. Questions asked over Earth Observation Linked Data have an inherent spatio-temporal dimension and can be represented using GeoSPARQL. This paper seeks to study and analyze the use of RNN-based neural machine translation with attention for transforming natural language questions into GeoSPARQL queries. Specifically, it aims to assess the feasibility of a neural approach for identifying and mapping spatial predicates in natural language to GeoSPARQL's topology vocabulary extension including - Egenhofer and RCC8 relations. The queries can then be executed over a triple store to yield answers for the natural language questions. A dataset consisting of mappings from natural language questions to GeoSPARQL queries over the Corine Land Cover(CLC) Linked Data has been created to train and validate the deep neural network. From our experiments, it is evident that neural machine translation with attention is a promising approach for the task of translating spatial predicates in natural language questions to GeoSPARQL queries., Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2020

This talk discusses the development of a spatiotemporal data model for an urban gazetteer. The function of gazetteers is to obtain descriptions uniquely identifying places referred to in discourse. Often, they are lists of places containing place name, feature type and geographical extent. Contemporary digital gazetteers (e.g. World Historical Gazetteer and Pleiades) are valuable tools for geographical knowledge of the past and the structuring of humanities data. However, scholars and GLAM (Galleries, Libraries, Archives and Museums) specialists often require information about entities on an intra-city scale. This presentation explores the model and implementation of an urban gazetteer using CIDOC CRM as a top-level ontology. The model will closely follow international gazetteer standards (i.e. Linked Places Format) in order to ensure interoperability with other gazetteer datasets. To move towards a FAIR (Findable, Accessible, Interoperable, and Reusable) approach, humanities data from the urban gazetteer will be published as Linked Open Data (LOD) and searchable via (Geo)SPARQL.

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology(RSSO)&mdash, a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.

GeoSPARQL is an important standard for the geospatial linked data community, given that it defines a vocabulary for representing geospatial data in RDF, defines an extension to SPARQL for processing geospatial data, and provides support for both qualitative and quantitative spatial reasoning. However, what the community is missing is a comprehensive and objective way to measure the extent of GeoSPARQL support in GeoSPARQL-enabled RDF triplestores. To fill this gap, we developed the GeoSPARQL compliance benchmark. We propose a series of tests that check for the compliance of RDF triplestores with the GeoSPARQL standard, in order to test how many of the requirements outlined in the standard a tested system supports. This topic is of concern because the support of GeoSPARQL varies greatly between different triplestore implementations, and the extent of support is of great importance for different users. In order to showcase the benchmark and its applicability, we present a comparison of the benchmark results of several triplestores, providing an insight into their current GeoSPARQL support and the overall GeoSPARQL support in the geospatial linked data domain.

Various models have been developed to manage geographic data but most of them integrate heterogeneous techniques to support knowledge representation and reasoning. This is far from optimal because it requires mapping data between different representation formats; moreover, as it fragments knowledge, it limits the possibility to use complete information about the problem to be solved for the execution of inferences.

This article describes how public health information management is an interdisciplinary application which deals with cross linked application domains. Geospatial environment, place and meteorology parameters effect public health. Effective decision making plays a vital role and requires disease data analysis which in turn requires effective Public Health Knowledge Base (PHKB) and a strong efficient query engine. Ontologies enhance the performance of the retrieval system and achieve application interoperability. The current research aims at building PHKB through ontology based cross linked domain integration. It designs a dynamic GeoSPARQL query building from simple form based query composition. The spatial semantic multi criteria query engine is developed by identifying all possible query patterns considering the ontology elements and multi criteria from cross linked application domains. The research has adopted OGC, W3C, WHO and mHealth standards.

Map mashups, as a common way of presenting geospatial information on the Web, are generally created by spatially overlaying thematic information on top of various base maps. This simple overlay approach often raises geometric deficiencies due to geometric uncertainties in the data. This issue is particularly apparent in a multi-scale context because the thematic data seldom have synchronised level of detail with the base map. In this study, we propose, develop, implement and evaluate a relative positioning approach based on shared geometries and relative coordinates to synchronise geometric representations for map mashups through several scales. To realise the relative positioning between datasets, we adopt a Linked Data–based technical framework in which the data are organised according to ontologies that are designed based on the GeoSPARQL vocabulary. A prototype system is developed to demonstrate the feasibility and usability of the relative positioning approach. The results show that the appro...

The Netherlands' Cadastre, Land Registry and Mapping Agency – in short Kadaster – collects and registers administrative and spatial data on property and the rights involved. This includes for ships, aircraft and telecommunications networks. Doing so, Kadaster protects legal certainty. The Kadaster publishes many large authoritative datasets including several key registers of the Dutch Government (Topography, Addresses and Buildings). Furthermore Kadaster is also developing and maintaining the PDOK shared service, in which about 100 spatial datasets are being published in several formats, including an incredible amount of detailed geospatial objects. Geospatial objects include all plots of land, all buildings, all roads and all lampposts. These objects are spatially and/or conceptually related, but are maintained by different data curators. As a result these datasets are syntactically and architecturally disjoint, and using them together currently requires non-trivial human labor. In response to this, Kadaster is currently publishing its geo-spatial data assets as Linked Open Data. The standardized query language for Linked Open Geodata is GeoSPARQL. Unfortunately, current tooling does not support writing and evaluating GeoSPARQL queries. This paper presents GeoYASGUI, a GeoSPARQL editor and result-set viewer with IDE capabilities. GeoYASGUI is not a new software product, but an integration of and a collection of updates to existing Open Source libraries. With GeoYASGUI it becomes possible to query the rich Open Data assets of the Kadaster.

In this paper we present the system Ontop-spatial that is able to answer GeoSPARQL queries on top of geospatial relational databases, performing on-the-fly GeoSPARQL-to-SQL translation using ontologies and mappings. GeoSPARQL is a geospatial extension of the query language SPARQL standardized by OGC for querying geospatial RDF data. Our approach goes beyond relational databases and covers all data that can have a relational structure even at the logical level. Our purpose is to enable GeoSPARQL querying on-the-fly integrating multiple geospatial sources, without converting and materializing original data as RDF and then storing them in a triple store. This approach is more suitable in the cases where original datasets are stored in large relational databases (or generally in files with relational structure) and/or get frequently updated.

GIS information is clearly critical for disaster response planning. Formal planning can eliminate otherwise unanticipated conflicts in disaster response planning, particularly in distributed planning. Such conflicts may result in execution-time discovery and resolution, with sub-optimal results. This is especially true in conflicts based upon GIS information as the cases may be subtle, typically resulting from lack of resource access. However, GIS systems do not produce the kind of quantitative information needed for an interface with many other systems, especially planning. New technologies such as GeoSPARQL provide some hope but much work remains. This is a fertile area for research and development.

The concept of System Wide Information Management (SWIM) for global Air Traffic Management (ATM) is to achieve various information exchange among related stakeholders, such as information about flights, routes, surveillance, airports, aircrafts, and weather. Although standard information exchange models are adopted in the SWIM concept, these models are different for each type of information. Therefore, ontology and data-linking technologies are required for the semantic integration of heterogeneous data. In this paper, we focus on the geospatial information contained in various data, and propose a method for extending the geospatial representation of the existing aviation ontology. This enables advanced geospatial searches using geometry information and facilitates integration of heterogeneous data.

Geospatial information is indispensable for various real-world applications and is thus a prominent part of today&rsquo, s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal&rsquo, s metadata&mdash, a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.

Linked data (LD) is a technology to support publishing structured data on the web so that it may be interlinked. Building Information Modelling (BIM) is a key enabler to support integration of building data within the buildings life cycle (BLC). LD can therefore provide better access and more semantically useful querying of BIM data. The integration of BIM into the geospatial domain provides much needed contextual information about the building and its surroundings, and can support geospatial querying over BIM data. Creating GeoSPARQL queries for users who are non experts in semantic web technologies can be a challenge. In this paper we present a visualization tool built upon HTML5 and WebGL technologies that supports queries over linked data without the need to understand the resulting SPARQL queries. The interactive web interface can be quickly extended to support new use cases, for example, related to 3D geometries. The paper discusses the underlying data management, the methodology for uplifting several open data sources into Resource Description Framework (RDF), and the front-end implementation tested over a sample use case. Finally some discussion and future work is given, with a focus on how this tool can potentially support BIM integration.

In the field of geographic information science spatiotemporal information extraction from Web pages, especially unstructured documents, is one of the growing areas of the research. Abundant news is publishing every hour on the Web, which contains valuable spatiotemporal information for its users. It is cumbersome and time-consuming to search among unstructured texts and find events of interest. In this work, we will show you how to extract spatiotemporal and semantic entities and relationships representing in cultural event news reports and search within the information. Natural Language Processing (NLP) and automatic ontology population are tightly coupled, and together they make it possible to have Web documents semantically so that not only can machines comprehend the Web documents, but also as a result, users are able to find the ideal information with ease. A spatiotemporal semantic search engine enables us to answer, where and when an event will take place.

We describe the process and tools that we have used to generate and publish the BTN100 Linked Dataset, based on the original data from the Spanish Topographic Base (1:100.000 scale) from the Spanish Instituto Geográfico Nacional. We have taken into account the limitations and lessons learned from our initial experience on the generation and publication of Linked Data from a range of geographical sources in Spain, in 2010, and we have now refined the process in order to facilitate: declarative mappings for the transformations from existing open data (shapefiles), automation of transformations whenever there are changes in the original data sources, version control, and alignment with INSPIRE URIs. As a result of this transformation and publication process we have also updated the reference ontology for geographical features and aligned with general ontologies such as GeoSPARQL.

Ecological Networks (ENs) describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement. While various mathematical models of ENs have been defined, to our knowledge they focus on simulating ecosystems, but none of them deals with verifying whether any transformation proposals, as those collected in participatory decision-making processes for public policy making, are consistent with land usage restrictions.

Increasing ocean temperatures severely affects marine species and ecosystems. Among other things, rising temperatures cause coral bleaching and loss of breeding grounds for marine fish and mammals. Motivated by the need to understand better these global problems, researchers from all over the world generated huge amounts of oceanographic data during the last years. However, most of this data remain isolated in their own silos. One approach to provide safe accessibility to these silos is to map local, often database-specific identifiers, to shared global identifiers. This mapping can then be used to build interoperable knowledge graphs (KGs), where entities such as publications, people, places, specimens, environmental variables and institutions are all part of a single, shared knowledge space. This short paper describes one such effort, the OceanGraph KG, including the modeling and publication processes, and the current and prospective uses of the dataset. Fil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentina Fil: Rosales, Pablo Sebastián. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia "san Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz; Argentina Fil: Braun, Germán Alejandro. Universidad Nacional del Comahue; Argentina Fil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; Argentina Fil: Fillottrani, Pablo. Universidad Nacional del Sur; Argentina. Comisión de Investigaciones Científicas, Provincia de Buenos Aires (CICPBA); Argentina Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Checking the compliance of geospatial triplestores with the GeoSPARQL standard represents a crucial step for many users when selecting the appropriate storage solution. This publication presents the software which comprises the GeoSPARQL compliance benchmark — a benchmark which checks RDF triplestores for compliance with the requirements of the GeoSPARQL standard. Users can execute this benchmark within the HOBBIT benchmarking platform to quantify the extent to which the GeoSPARQL standard is implemented within the triplestore of interest. This enables users to make an informed decision when choosing an RDF storage solution and helps assess the general state of adoption of geospatial technologies on the Semantic Web.

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

The Land Registry and Mapping Agency of the Netherlands (‘Kadaster’ in Dutch) is developing an online publication platform for sharing its geospatial data assets called KDP (`Kadaster Data Platform’ in Dutch). One of the main goals of this platform is to better share geospatial data with the wider, web-oriented world, including its developers, approaches, and standards. Linked Open Data (W3C), GeoSPARQL (OGC), and Open APIs (OpenAPI Specification) are the predominant standardized approaches for this purpose. As a result, the most important spatial datasets of the Netherlands – including several key registries – are now being published as Linked Open Data that can be accessed through a SPARQL endpoint and a collection of REST APIs. In addition to providing raw access to the data, Kadaster Data Platform also offers developers functionalities that allow them to gain a better understanding about the contents of its datasets. These functionalities include various ways for viewing Linked Data . This paper focuses on two of the main components the Kadaster Data Platform is using for this purpose: FacetCheck and Data Stories.

The growing number of spatiotemporal datasets is an essential driver for bio-economy. Interoperability is needed to ensure efficient use of these data and had been addressed by standardization institutions, such as OGC and AIMS. Both of them promote the use of Semantic Web standards (e.g., GeoSPArql) as one pillar for interoperability [1]. A significant challenge to strengthen the utility of Semantic Web approaches is linking. Its central goal in the context of spatiotemporal datasets is the (semi-automatic) discovery of geospatial referents, such as events, areas, and places which are not yet linked or georeferenced. While the linking task is intrinsically challenging, it is especially resource- and time-consuming when processing and linking Semantic Big Data. This paper will demonstrate an approach which improves and automates linking Semantic Big Data and show its potential usage for bio-economy.

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

The Web is changing the way people share and communicate information because of emergence of various Web technologies, which enable people to contribute information on the Web. User-Generated Geo-Content (UGGC) is a potential resource of geographic information. Due to the different production methods, UGGC often cannot fit in geographic information model. There is a semantic gap between UGGC and formal geographic information. To integrate UGGC into geographic information, this study conducts an ontology-based process to bridge this semantic gap. This ontology-based process includes five steps: Collection, Extraction, Formalization, Mapping, and Deployment. In addition, this study implements this process on Twitter messages, which is relevant to Japan Earthquake disaster. By using this process, we extract disaster relief information from Twitter messages, and develop a knowledge base for GeoSPARQL queries in disaster relief information.

The Japanese Statistics Center began publishing a statistical linked open data (LOD) site in 2016. The data currently consists of approximately 1.3 billion triples. The publication of statistical data as LOD enables datasets and categorizations to be clarified. This allows users not only to search objective data easily, but also to combine the data with other domestic or international data. This paper first introduces a design policy for LOD and a method for representing geographic areas. Then, it explains the method used to query the LOD by using SPARQL or GeoSPARQL, and provides one example application.

While existing Linked Datasets provide detailed representations of Cultural Heritage objects, the locations where the objects originate from is often not accurately represented. Countries, municipalities, and excavation sites are commonly represented by geospatial points, and the fact that countries and municipalities change their geometry over time is not reflected in the data. We present nlGis, a collection of existing geo-historic datasets that are now published as Linked Open Data. The datasets in nlGis contain detailed geographic information about historic regions, with an emphasis on the Netherlands. We describe the creation of this Linked Geodataset and how it can be used to enrich Cultural Heritage data. We also distill several 'lessons learned' that can guide future attempts at publishing detailed Linked Geodata in the Cultural Heritage domain.

The Web and its capabilities can be employed as a tool for data and information integration if comprehensive datasets and appropriate technologies and standards enable the web with interpretation and easy alignment of data and information. Semantic Web along with the spatial functionalities enable the web to deal with the huge amount of data and information. The present study investigate the advantages and limitations of the Spatial Semantic Web and compare its capabilities with relational models in order to build a spatial data infrastructure. An architecture is proposed and a set of criteria is defined for the efficiency evaluation. The result demonstrate that when using the data with special characteristics such as schema dynamicity, sparse data or available relations between the features, the spatial semantic web and graph databases with spatial operations are preferable.

Many institutions will be providing data to the National Spatial Data Infrastructure (NSDI). Current technical background of the NSDI is based on syntactic web services. It is expected that this will be replaced by semantic web services. The quality of the data provided is important in terms of the decision-making process and the accuracy of transactions. Therefore, the data quality needs to be tested. This topic has been neglected in Turkey. Data quality control for NSDI may be done by private or public “data accreditation” institutions. A methodology is required for data quality evaluation. There are studies for data quality including ISO standards, academic studies and software to evaluate spatial data quality. ISO 19157 standard defines the data quality elements. Proprietary software such as, 1Spatial’s 1Validate and ESRI’s Data Reviewer offers quality evaluation based on their own classification of rules. Commonly, rule based approaches are used for geospatial data quality check. In this study, we look for the technical components to devise and implement a rule based approach with ontologies using free and open source software in semantic web context. Semantic web uses ontologies to deliver well-defined web resources and make them accessible to end-users and processes. We have created an ontology conforming to the geospatial data and defined some sample rules to show how to test data with respect to data quality elements including; attribute, topo-semantic and geometrical consistency using free and open source software. To test data against rules, sample GeoSPARQL queries are created, associated with specifications.

Urban planning data are of big importance to both general public and domain experts like civil engineers, architects or urban planning specialists. Typically, the data are scattered within many datasets containing both geographical knowledge and taxonomical knowledge. Features representing same objects in two different datasets often do not have any connection except geometry. Complex queries over multiple datasets are rather complicated and data have to be preformed, due to the distributed nature of such data, as well as their complexity. We present a case study on ontology modeling of urban planning data of the City of Prague. We discuss the ontological and spatial nature of the domain, followed by the design and formalization of the context-sensitive Urban ontology together with GeoSPARQL queries showing the usage of the ontology for dataset exploration and data search.

Currently, we are witnessing an exponential growth in the amount of data being generated and captured at multiple locations. This trend will continue over the next years. Hence, we have envisioned a scenario in which many objects will be referencing to or generating location information. Thus, the need for appropriately managing geospatial data is evident. In this paper, we present our vision for an integral Geo Linked Data platform; pointing out the current limitations and challenges in the GeoRDFization, Storage, Query Federation, and Visualization of data with an inherent spatial context.

Collections of linguistic and dialect data often lack a semantic description and the ability to establish relations to external datasets, from e.g. demography, socio-economics, or geography. Based on existing projects—the Database of Bavarian Dialects in Austria and exploreAT!—this paper elaborates on a spatio-temporal Linked Data model for representing linguistic/dialect data. Here we focus on utilizing existing data and publishing them using a virtual RDF graph. Additionally, we exploit external data sources like DBPedia and geonames.org, to specify the meaning of dialect records and make use of stable geographical placenames. In the paper we highlight a spatio-temporal modeling and representation of linguistic records relying on the notion of a discrete lifespan of an object. Based on a real-world example—using the lemma “Karotte” (engl. carrot) we show how the usage of a specific dialect word (“Karottn”) changes from 1916 until 2016—by exploiting the expressive power of GeoSPARQL.

Building Information Modelling (BIM) is a key enabler to support integration of building data within the buildings life cycle and is an important aspect to support a wide range of use cases, related to building navigation, control, sustainability, etc. Open BIM faces several challenges related to standardization, data interdependency, data access, and security. In addition to these technical challenges, there remains the barrier among BIM developers who wish to protect their intellectual property, as full 3D BIM development requires expertise and effort. This means that there is often limited availability of BIM models. In Ireland, the Ordnance Survey Ireland (OSi) has a substantial dataset which includes not only GIS data (polygon footprint, geodetic coordinate), but also additional building specific data (form and function). In this paper we demonstrate the use of an applied and tested methodology for uplifting GIS data (relational data) into RDF (GeoSPARQL and OSi ontology) and demonstrate how this data is used for interlinking to other building data with an initial, simple exploratory example, taken from DBpedia. By interlinking building data and making it available, new insights about buildings in Ireland can be made, currently not possible due to lack of availability of data. This is an important step towards the iterative integration of ever more complex BIM models into the wider web of data to support the aforementioned use cases.

The concept of "location" provides one a useful dimension to explore, align, combine, and analyze data. Though one can rely on bespoke GIS systems to conduct their data analyses, we aim to investigate the feasibility of using Semantic Web technologies to leverage the exploration and enrichment of data in CSV files with the vast amount of geographic and geospatial data that are available on the Linked Data Web. In this paper, we propose a lightweight method and set of tools for: uplift - transforming non-RDF resources into RDF documents; creating links between RDF datasets; client-side processing of geospatial functions; and downlift - transforming (enriched) RDF documents back into a non-RDF format. With this approach, people who wish to avail of the spatial dimension in data can do so from their client (e.g., in a browser) without the need to rely on bespoke technology. This could be of great utility for decision makers and scholars, amongst others. We applied our approach on datasets that are hosted on the Irish open data portal, and combined it with authoritative geospatial data made available by Ordnance Survey Ireland (OSi). Albeit aware that our approach cannot compete with specialist tools, we do demonstrate its feasibility. Though currently conducted for enriching datasets hosted on the Irish open data portal, future work will look into broader governance and provenance aspects of geospatial data enriched dataset management.

The Coordinated Online Information Network (COIN) is a spatial data infrastructure (SDI) which provides an online network of resources to share, use and integrate information of geographic locations in North Canada. COIN incorporates semantic web technology that integrates, publishes and visualizes time series water data allowing users to access a multitude of datasets in order to compare the data and draw conclusions. COIN utilizes a number of standards from OGC (Open Geospatial Consortium) and W3C (Resource Description Framework, RDF, Web Ontology Language OWL, SPARQL query language for RDF) and GeoSPARQL for geospatial query). COIN benefits from generic ontologies transforming data into semantics, enriching data sets and making the data available and interoperable via WFS and WMS standards. These principles facilitate publication and exchange of data across the web, increasing transparency and interpretability. Through modernized data submission and retrieval we hope to break down the silos of data, allowing users to visualize time series water quality and hydrometric data from multiple sources to increase knowledge in relationship to impacts on Yukon water.

Ecological Networks (ENs) are a way to describe the structures of existing real ecosystems and to plan their expansion, conservation and improvement. In this work, we present a model to represent the specifications for the local planning of ENs in a way that can support reasoning, e.g., to detect violations within new proposals of expansion, or to reason about improvements of the networks. Moreover, we describe an OWL ontology for the representation of ENs themselves. In the context of knowledge engineering, ENs provide a complex, inherently geographic domain that demands for the expressive power of a language like OWL augmented with the GeoSPARQL ontology to be conveniently represented. More importantly, the set of specification rules that we consider (taken from the project for a local EN implementation) constitute a challenging problem for representing constraints over complex geographic domains, and evaluating whether a given large knowledge base satisfies or violates them.

By definition, a gazetteer is a directory of place names or toponyms. However, now, more and more gazetteers are becoming complex databases. Since they increasingly include other attributes of the named features, they tend to become toponym ontologies. However, there is also the problem of languages; we touched on this briefly in the chapter dealing with ontologies but since a lot of places have different names in different languages, this problem is crucial.

Rapid retrieval of spatial information is critical to ensure that emergency supplies and resources can reach the impacted areas in the most efficient manner. However, it remains challenging to find out the needed spatial information efficiently because of the intensive geocomputation processes involved and the heterogeneity of spatial data. It is quite cost prohibitive to query the spatial information from geographical knowledge bases containing complex topological relationships. This research introduces a Map-Reduce based parallel approach for improving the query performance of a geospatial ontology for disaster response. The approach focuses on parallelizing the spatial join computations of GeoSPARQL queries. The proposed parallel approach makes full use of data/task parallelism for spatial queries. The results of some initial experiments show that the proposed approach can reduce individual spatial query execution time by taking advantage of parallel processes. The proposed approach, therefore, may afford a large number of concurrent spatial queries in disaster response applications.

Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data.

We propose an OBDA approach for accessing geospatial data stored in relational databases, using the OGC standard GeoSPARQL and R2RML or OBDA mappings. We introduce extensions to an existing SPARQL-to-SQL translation method to support GeoSPARQL features. We describe the implementation of our approach in the system ontop-spatial, an extension of the OBDA system Ontop for creating virtual geospatial RDF graphs on top of geospatial relational databases. We present an experimental evaluation of our system using and extending a state-of-the-art benchmark. To measure the performance of our system, we compare it to a state-of-the-art geospatial RDF store and confirm its efficiency.

We report on the Linked Data platform developed for the administrative boundaries governed by the Ordnance Survey Ireland (OSi), as they wished to serve this data as an authoritative Linked Open Data dataset on the Web. To implement this platform, we have adopted best practices and guidelines from the industry and academia. We demonstrate how this dataset can be combined with other datasets to add a spatial component to information. We believe that the publication of this dataset not only provides opportunities for third parties (including scholars) in their activities, but that this outcome of this initiative is of importance, as the OSi made the authoritative dataset available. With the current platform deployed, future work will include the inclusion of other (closed) datasets and the investigation of access mechanisms.