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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.

An introduction to the Semantic Web for GIS professional and scientists. Presents apath to FAIR geo-spatial data with W3C and OGC standards.

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 and exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact presentation. This paper describes 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.

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.

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.

Urbanization is a continuous evolution process that is currently studied by a number of researchers. Multi-source and multidimensional city information models are often used to understand the ever-changing urban landscape. These models may encounter issues with interoperability and data-loss if conversion is required for integration. Today we can base ourselves on conceptual models which can help deal with data losses during conversion and may also help preserve data interoperability. This kind of model-driven approach can be useful as common representation. Recently, a movement towards graph and semantic based data representations has also grown in popularity to respond to these issues. As a first step, we consider CityGML, a common standard that can be used to represent 3D urban information. We propose a strategy for converting the semantics of CityGML conceptual model into ontologies and later to semantic web formats to facilitate integration. In addition, we propose a method for converting and storing CityGML instances into RDF individuals that respect the generated ontology. This proposed approach overcomes the loss of semantic information resulting from the direct translation of different types of data into graphs such as RDF, L'urbanisation est un processus d'évolution continue qui est actuellement étudié par nombre de chercheurs. Les modèles d'information de la ville, multi-sources et multidimensionnels, sont souvent utilisés pour comprendre le paysage urbain, qui est en constante évolution. Ces modèles peuvent cependant poser des problèmes d'interopérabilité et de perte de données lors de conversions, souvent nécessaires pour permettre leur intégration. Aujourd'hui, nous pouvons nous baser sur des modèles conceptuels qui peuvent aider à traiter les pertes de données lors de conversions, et peuvent également contribuer à préserver l'interopérabilité des données. Ce type d'approche modèle-centrée permet de définir une représentation commune. Récemment, un mouvement vers la représentation de données basée sur les graphes et la sémantique a gagné en popularité pour répondre aux problèmes d'interopérabilité. Dans un premier temps, pour illustrer notre approche, nous considérons CityGML, un standard de représentation des informations urbaines en 3D. Nous proposons une stratégie pour convertir la sémantique du modèle conceptuel de CityGML vers des ontologies et, ensuite, en formats web sémantiques pour faciliter l'intégration. Nous proposons également une méthode pour convertir et stocker les instances CityGML en individus RDF qui respectent l'ontologie générée. L'approche proposée permet de pallier le déficit d'information sémantique résultant de la traduction directe de différents types de données en graphe, tels que RDF.

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.

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.

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.

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.

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.

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.

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.

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.

The Semantic Web eases data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be interactively transformed and represented adhering to the RDF data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this data is interlinked with other spatial data sets, how it can be made accessible for machines according to the Linked Data paradigm and for humans by means of several applications, including a faceted geo-browser. The spatial data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.

As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. GeoSPARQL, a forthcoming OGC standard, attempts to unify data access for the geospatial Semantic Web. As authors of the Parliament triple store and contributors to the GeoSPARQL specification, we are particularly interested in the issues of geospatial data access and indexing. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on GeoSPARQL. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and finally our implementation of GeoSPARQL in the Parliament triple store.

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

The INSPIRE Directive by the European Commission sets the legal and technical foundations towards interoperable Spatial Data Infrastructures (SDIs) across Europe. EU member states are already providing such services for several geospatial data themes (e.g.,?transportation networks, administrative units). Unfortunately, the INSPIRE ecosystem has been largely disjoint from the Semantic Web, without any means to repurpose existing SDIs as high-quality data sources, and thus multiply their value through interlinking, reasoning and inferencing. In this paper, we introduce a methodology that can assist stakeholders in exposing INSPIRE-aligned SDIs on the Semantic Web according to the recent GeoSPARQL standard. We develop methods for discovering INSPIRE data through a virtual SPARQL endpoint over existing INSPIRE catalogue services. Further, we implement a suite of tools for automatically transforming INSPIRE data and metadata into RDF triples with geometries. The compiled geographic and thematic information can then be loaded into semantic repositories for querying or interlinked with other data. Our open-source solutions essentially repurpose existing INSPIRE SDIs, so as to promote uptake and facilitate their reuse in practice. Finally, as a case study, we report our experience in validating this approach on a real-world SDI with publicly available data for Greece in order to expose its contents through (Geo)SPARQL endpoints.

We introduce an approach to semantically represent and query raster data in a Semantic Web graph. We extend the GeoSPARQL vocabulary and query language to support raster data as a new type of geospatial data. We define new filter functions and illustrate our approach using several use cases on real-world data sets. Finally, we describe a prototypical implementation and validate the feasibility of our approach.