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Semantic Spatial Web
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Semantic Spatial Web

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 26767

Special Issue Editors

Dr. Rob Brennan

E-Mail Website
Guest Editor
ADAPT, School of Computer Science, University College Dublin, D02 PN40 Dublin, Ireland
Interests: data governance; AI governance; knowledge graphs; data quality; data value; data privacy
Special Issues, Collections and Topics in MDPI journals
Dr. Brian Davis

E-Mail Website
Guest Editor
ADAPT, School of Computing, Dublin City University, Dublin 9, Ireland
Interests: NLP for the semantic web; ontology based information extraction; semantic annotation; natural language generation
Dr. Armin Haller

E-Mail Website
Guest Editor
Research School of Management, Australian National University, PAP Moran Building 26B, Acton 2601, Australia
Interests: ontologies; knowledge graph engineering; knowledge management; knowledge learning; data quality; linked data; Internet of Things
Dr. Beyza Yaman

E-Mail Website
Guest Editor
ADAPT Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
Interests: geospatial linked data; geospatial data quality; data matching

Special Issue Information

Dear Colleagues,

Spatial data are vital for many application scenarios, such as navigation, logistics, and tourism. The last decade has seen a steady increase in geospatial linked data deployment. Large numbers of linked data datasets contain geospatial aspects (e.g., DBpedia, Wikidata), and there is increasing deployment of Linked Data for national geospatial infrastructure (e.g., Geohive in Ireland, Kadaster in the Netherlands) and now in the Architecture, Engineering and Construction (AEC) sector and for Internet of Things deployments. New applications integrating geospatial linked data into public and private organizations have immense potential for a social, economic, and scientific impact. Geospatial linked data allow discovery and access using the standard mechanisms of the Web and simplify the process of generating interoperable geospatial infrastructure. This is especially relevant to delivering the promise of the INSPIRE directive in Europe. Moreover, geospatial information systems benefit from linked data principles in building the next generation of spatial data applications, such as federated smart buildings and self-piloted vehicles.

The goal of this Special Issue is to provide an opportunity for the geospatial linked data community to focus on the emerging need for effective and efficient production, management, and utilization of geospatial information as linked data. Emphasis will be given to works describing novel methodologies, algorithms, and tools that advance the current state-of-the-art with respect to efficiency or effectiveness. Thus, we invite papers related to the challenges and solutions proposed to deal with geospatial linked data, especially for building high-quality, adaptable, geospatial infrastructures and novel applications. We aim at demonstrating the latest approaches and implementations, as well as discussing solutions to the challenges and issues arising from research and industrial organizations.

  • Spatial linked data vocabularies and standards (GeoSPARQL, INSPIRE, W3C, OGC, ISO)
  • Extraction/transformation of geospatial linked data from native geospatial data sources
  • Integration (schema mapping, interlinking, fusion) techniques for geospatial RDF data
  • Enrichment, quality, and evolution of linked data with spatial information
  • Machine learning improving geospatial linked data processing
  • Distributed solutions for geospatial linked data management (storing, querying, mapping)
  • Algorithms and tools for large scale, scalable geospatial linked data management
  • Efficient indexing and querying of geospatial linked data
  • Geospatial-specific reasoning on RDF data
  • Ranking techniques on querying geospatial RDF data
  • Advanced querying capabilities on geospatial RDF data
  • Benchmarking of geospatial linked data applications
  • Geospatial linked data in social web platforms and applications
  • Visualization models/interfaces for browsing/authoring/querying geospatial linked data
  • Natural language processing (NL) applications for the spatial semantic web
  • Ontology based information extraction and semantic annotation for geospacial semantic web
  • Linking entities in content to geospatial linked data
  • Natural language interfaces to geospatial linked data (creating, querying and accessing geospatial RDF data)
  • Real-world applications/use cases/paradigms using geospatial linked data
  • Evaluation/comparison of tools/libraries/frameworks for geospatial linked data
  • Data governance models for geospatial linked data
  • Building information modeling for building life cycle (GIS data, geographical data, and so forth)
  • Linking building data to geospatial linked data
  • Architectural and construction data for geospatial linked data
  • Geospatial linked data for smart cities
  • Crowdsourced spatial linked data

Dr. Rob Brennan
Dr. Brian Davis
Dr. Armin Haller
Dr. Beyza Yaman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial/geospatial linked data
  • geospatial data quality
  • geospatial data standards
  • NLP applications for spatial data
  • architecture
  • engineering and construction

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Published Papers (7 papers)

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Research

18 pages, 3732 KiB  
Article
Semantic Integration of Raster Data for Earth Observation on Territorial Units
by Ba-Huy Tran, Nathalie Aussenac-Gilles, Catherine Comparot and Cassia Trojahn
ISPRS Int. J. Geo-Inf. 2022, 11(2), 149; https://doi.org/10.3390/ijgi11020149 - 19 Feb 2022
Cited by 3 | Viewed by 3201
Abstract
Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image [...] Read more.
Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to territorial units (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a semantic extraction, transformation and load (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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30 pages, 1719 KiB  
Article
GeoSPARQL 1.1: Motivations, Details and Applications of the Decadal Update to the Most Important Geospatial LOD Standard
by Nicholas J. Car and Timo Homburg
ISPRS Int. J. Geo-Inf. 2022, 11(2), 117; https://doi.org/10.3390/ijgi11020117 - 7 Feb 2022
Cited by 16 | Viewed by 4696
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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12 pages, 1581 KiB  
Communication
Interoperability and Integration: An Updated Approach to Linked Data Publication at the Dutch Land Registry
by Alexandra Rowland, Erwin Folmer, Wouter Beek and Rob Wenneker
ISPRS Int. J. Geo-Inf. 2022, 11(1), 51; https://doi.org/10.3390/ijgi11010051 - 10 Jan 2022
Cited by 4 | Viewed by 2881
Abstract
Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully [...] Read more.
Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster’s larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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15 pages, 2617 KiB  
Article
Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions
by Muhua Wang, Xueying Zhang, Deen Feng, Yipeng Wang, Wei Tang and Peng Ye
ISPRS Int. J. Geo-Inf. 2021, 10(12), 835; https://doi.org/10.3390/ijgi10120835 - 15 Dec 2021
Cited by 5 | Viewed by 3663
Abstract
The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph [...] Read more.
The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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18 pages, 1036 KiB  
Article
Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy
by Christian Zinke-Wehlmann and Amit Kirschenbaum
ISPRS Int. J. Geo-Inf. 2021, 10(10), 712; https://doi.org/10.3390/ijgi10100712 - 19 Oct 2021
Cited by 3 | Viewed by 2075
Abstract
Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the [...] Read more.
Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the web. The present paper introduces Geo-L, a system for the discovery of RDF spatial links based on topological relations. Experiments show that the proposed system improves state-of-the-art spatial linking processes in terms of mapping time and accuracy, as well as concerning resources retrieval efficiency and robustness. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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24 pages, 5267 KiB  
Article
Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method
by Zhi-Wei Hou, Cheng-Zhi Qin, A-Xing Zhu, Yi-Jie Wang, Peng Liang, Yu-Jing Wang and Yun-Qiang Zhu
ISPRS Int. J. Geo-Inf. 2021, 10(9), 605; https://doi.org/10.3390/ijgi10090605 - 14 Sep 2021
Cited by 3 | Viewed by 3059
Abstract
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may [...] Read more.
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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19 pages, 555 KiB  
Article
A GeoSPARQL Compliance Benchmark
by Milos Jovanovik, Timo Homburg and Mirko Spasić
ISPRS Int. J. Geo-Inf. 2021, 10(7), 487; https://doi.org/10.3390/ijgi10070487 - 16 Jul 2021
Cited by 19 | Viewed by 4978
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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