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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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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 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.

The SDI4Apps project has collected a large number of points of interest (POIs). The Smart Points of Interest (SPOI) represents a seamless and open resource of POIs covering all the world. Its principal target has been to provide information for cycling as Linked data together with other data sets containing road network. But the current version can be used for any purposes related to tourism. The article presents the data model for POIs as a basis for harmonization of external data sources into this data model. The current version of the SPOI data set includes a harmonized combination of selected OpenStreetMap data, GeoNames.org, experimental geo-ontologies developed at the University of West Bohemia and local data. The data model follows the recommendations for RDF data sets, semantic data, and Linked Data as well as the data model published in Points of Interest Core. The SPOI knowledge base complies with the 5-star rating system of Linked Open Data. The data model re-uses several important, respected and standardized formats and vocabularies such as XML, XML Schema, RDF, RDFS, SKOS (Simple Knowledge Organization System), GeoSPARQL or FOAF (Friend of a Friend).

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.

An immense wealth of data is already accessible through the Semantic Web and an increasing part of it also has geospatial context or relevance. Although existing technology is mature enough to integrate a variety of information from heterogeneous sources into interlinked features, it still falls behind when it comes to representation and reasoning on spatial characteristics. It is only lately that several RDF stores have begun to accommodate geospatial entities and to enable some kind of processing on them. To address interoperability, the OGC has recently adopted the GeoSPARQL standard, which defines a vocabulary for representing geometric types in RDF and an extension to the SPARQL language for formulating queries. In this paper, we provide a comprehensive review of the current state-of-the-art in geospatially-enabled semantic data management. Apart from an insightful analysis of the available architectures in industry and academia, we conduct an evaluation study on prominent RDF stores with geospatial support. We also compare their performance and attested capabilities to renowned DBMSs widely used in geospatial applications. We introduce a methodology suitable to assess RDF stores for robustness against large geospatial datasets, and also for expressiveness on a variety of queries involving both spatial and thematic criteria. As our findings demonstrate, the potential for query optimization, advanced indexing schemes, and spatio-semantic extensions is significant. Towards this goal, we point out several challenging issues for joint research by the GIS and Semantic Web communities.

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014. Cadastral data is one of the more important types of geospatial data. Taking into account the importance of these data, several international bodies have worked for creating a standardised model for land administration. However, in spite of existing efforts, there are several open issues for the development of a harmonized vision of cadastral data. Taking into account this scenario, Linked Open Data may allow addressing some of these challenges, by proposing best practices for exposing, sharing, and integrating data on the Web. This paper shows a use case where two cadastral information sources are semantically integrated according to Linked Data principles. These sources belong to different Colombian cadastral producers and are characterized by different heterogeneity issues. Herein, we describe an implementation of Linked Data principles in the cadastral domain using LADM standard (ISO 19152) and GeoSPARQL. Besides, our original data are enriched with different dataset of Linked Data cloud (LinkedGeoData and GeoNames).

The need to better integrate and link various isolated data sources on the web has been widely recognized and is tackled by the Linked Open Data (LOD) initiative. One of the problems to address is the issue of publishing and subsequently exploiting the data as LOD, due to reasons of data size and performance of the respective queries and to the publication complexity. This work addresses the size and performance issues by adapting the cloud as a hosting platform for LOD publication services so as to exploit its scalability and elasticity capabilities. The publication complexity issue is addressed by proposing a Linked Open Data-as-a-Service approach offering an integrated service based API for (semi)automatic publication of relational data as LOD and subsequent querying and updating capabilities.

Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.

Incomplete information has been studied in-depth in relational databases and knowledge representation. It is also an important issue in Semantic Web frameworks such as RDF, description logics, and OWL 2. In [6], we introduced RDFi, an extension of RDF for representing incomplete information using constraints. We defined a semantics for RDFi and studied SPARQL query evaluation in this framework. Given the current interest in publishing geospatial datasets as linked data (e.g., by Ordnance Survey in the UK), RDFi is an excellent framework for encoding, possibly incomplete, qualitative and quantitative geospatial information which is found in these published datasets. RDFi is also interesting because when the constraint language used can express the topological relations of RCC-8 [8], the recent OGC standard GeoSPARQL [7] for querying geospatial information expressed in RDF, becomes a special case of RDFi.

In order to employ the Web as a medium for data and information integration, comprehensive datasets and vocabularies are required as they enable the disambiguation and alignment of other data and information. Many real-life information integration and aggregation tasks are impossible without comprehensive background knowledge related to spatial features of the ways, structures and landscapes surrounding us. In this paper we contribute to the generation of a spatial dimension for the Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be transformed and represented adhering to the RDF data model. We describe how this data can be 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 a faceted geo-data browser.

We present an Ontology-based data access technique for the integration of geospatial data from different sources and different formats using ontologies and mappings. Our technique is implemented in the system Ontop-spatial, which enables the execution of GeoSPARQL queries over geospatial relational databases storing vector or raster data by performing on-the-fiy GeoSPARQL-to-SQL translation using ontologies and mappings. Ontop-spatial is generally more efficient than traditional geospatial RDF stores. Linked geospatial data can then be visualised on the map using Sextant, an online tool for browsing and visualisation of linked geospatial data.

With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient.

Large amounts of geospatial data have been made available recently on the linked open data cloud and on the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper we go beyond these approaches to offer a question answering service on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Qanary question answering architecture to provide benefits for future research tasks. We give a detailed description of the architecture of the system, its underlying algorithms and its evaluation using a set of 201 natural language questions.

With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient.