Geospatial Knowledge Graph

Topic Information

Dear Colleagues,

In the past two decades, the knowledge-graph-based approach has been widely used in GIScience, since it provides an explicit and formalized representation of geospatial data. At the core of solutions based on knowledge graphs, we typically have an ontology to provide semantics to the data. Many researchers proposed geo-ontologies and geospatial knowledge graphs to represent domain knowledge, to support geospatial data integration, and to facilitate geospatial analysis. The purpose of this Topic is to collect high-quality research results about geospatial knowledge graphs, ranging from foundational theories to practical algorithm and tools, and to novel applications. The areas to be covered in this research topic may include, but are not limited to:

- Geospatial knowledge graph construction;

- Geospatial data integration through knowledge graphs;

- Geospatial ontologies;

- Querying geospatial knowledge graphs;

- Deep learning over geospatial knowledge graphs;

- Visualization of geospatial knowledge graphs;

- Systems for geospatial knowledge graphs;

- Applications of geospatial knowledge graphs.

Dr. Guohui Xiao
Dr. Yu Feng
Dr. Linfang Ding
Dr. Younes Hamdani
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geomatics geomatics	-	-	2021	18.6 Days	CHF 1000	Submit
ISPRS International Journal of Geo-Information ijgi	3.4	6.2	2012	35.5 Days	CHF 1700	Submit

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Published Papers (6 papers)

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All Geomatics IJGI

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23 pages, 3935 KiB  

Open AccessArticle

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

by Xinya Lei, Yuewei Wang, Wei Han and Weijing Song

ISPRS Int. J. Geo-Inf. 2024, 13(3), 88; https://doi.org/10.3390/ijgi13030088 - 11 Mar 2024

Viewed by 934

Abstract

Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge [...] Read more.

Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge of integrating multi-source information with various spatial and temporal scales. To address this challenge, we propose a new information model for storm surge hazard events, involving a two-step process. First, a hazard event ontology is designed to model the components and hierarchical relationships of hazard event information. Second, we utilize multi-scale time segment integer coding and geographical coordinate subdividing grid coding to create a spatio-temporal framework, for modeling spatio-temporal features and spatio-temporal relationships. Using the 2018 typhoon Mangkhut storm surge event in Shenzhen as a case study and the hazard event information model as a schema layer, a storm surge event knowledge graph is constructed, demonstrating the integration and formal representation of heterogeneous hazard event information and enabling the fast retrieval of disasters in a given spatial or temporal range. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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17 pages, 5862 KiB  

Open AccessArticle

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

by Hanme Jang, Kiyun Yu and Jiyoung Kim

ISPRS Int. J. Geo-Inf. 2024, 13(2), 52; https://doi.org/10.3390/ijgi13020052 - 9 Feb 2024

Viewed by 1321

Abstract

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot [...] Read more.

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like ‘loc’, ‘indoorgml-lite’, and ‘bloc’ to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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21 pages, 4351 KiB  

Open AccessArticle

Cultural Itineraries Generated by Smart Data on the Web

by Cosmo Capodiferro, Massimo De Maria, Mauro Mazzei, Matteo Spreafico, Oleg V. Bik, Armando L. Palma and Anna V. Solovyeva

ISPRS Int. J. Geo-Inf. 2024, 13(2), 47; https://doi.org/10.3390/ijgi13020047 - 3 Feb 2024

Viewed by 1305

Abstract

The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some [...] Read more.

The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential for the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has an artistic and cultural heritage unique in the world and the Italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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17 pages, 2771 KiB  

Open AccessArticle

Geographic Knowledge Base Question Answering over OpenStreetMap

by Jonghyeon Yang, Hanme Jang and Kiyun Yu

ISPRS Int. J. Geo-Inf. 2024, 13(1), 10; https://doi.org/10.3390/ijgi13010010 - 26 Dec 2023

Viewed by 1521

Abstract

In recent years, question answering on knowledge bases (KBQA) has emerged as a promising approach for providing unified, user-friendly access to knowledge bases. Nevertheless, existing KBQA systems struggle to answer spatial-related questions, prompting the introduction of geographic knowledge ba se question answering (GeoKBQA) [...] Read more.

In recent years, question answering on knowledge bases (KBQA) has emerged as a promising approach for providing unified, user-friendly access to knowledge bases. Nevertheless, existing KBQA systems struggle to answer spatial-related questions, prompting the introduction of geographic knowledge ba se question answering (GeoKBQA) to address such challenges. Current GeoKBQA systems face three primary issues: (1) the limited scale of questions, restricting the effective application of neural networks; (2) reliance on rule-based approaches dependent on predefined templates, resulting in coverage and scalability challenges; and (3) the assumption of the availability of a golden entity, limiting the practicality of GeoKBQA systems. In this work, we aim to address these three critical issues to develop a practical GeoKBQA system. We construct a large-scale, high-quality GeoKBQA dataset and link mentions in the questions to entities in OpenStreetMap using an end-to-end entity-linking method. Additionally, we develop a query generator that translates natural language questions, along with the entities predicted by entity linking into corresponding GeoSPARQL queries. To the best of our knowledge, this work presents the first purely neural-based GeoKBQA system with potential for real-world application. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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15 pages, 5007 KiB  

Open AccessArticle

Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

by Tianle Li, Minrui Zheng, Xiaoli Wang and Xinqi Zheng

ISPRS Int. J. Geo-Inf. 2023, 12(11), 458; https://doi.org/10.3390/ijgi12110458 - 9 Nov 2023

Viewed by 1447

Abstract

Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) [...] Read more.

Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) data of Shenzhen city to implement the funtion2vec model based on the node2vec model and urban tree theory. In this model, we first divide UFE patterns into three categories: Function Replace (FR), Function Newly Added (FNA), and Function Vanishing (FV). Then, we calculate the correlation between those UFE patterns using their functional vectors, resulting in a graph structure representing the urban function evolution network (UFEN). Based on our case study, we obtained the following conclusions: (1) From 2013 to 2022, the UFE in Shenzhen was primarily dominated by FR (89.44%). (2) FV and FNA exhibit a long-tailed distribution, adhering to the 20–80 law. (3) Through the UFEN based on FR, healthcare services are well suited to form mutual complementarities with other functions; science, education, and cultural services demand a higher complementarity with other functions; administrative offices exhibit a strong diversity in their evolutionary patterns; and the integration of transportation hubs with other functions results in a significantly deviating urban function evolution from its original pattern. The above conclusions suggest that function2vec can well express UFE in emerging cities by adding spatial correlation in UFE. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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30 pages, 32076 KiB  

Open AccessArticle

An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs

by Younes Hamdani, Guohui Xiao, Linfang Ding and Diego Calvanese

ISPRS Int. J. Geo-Inf. 2023, 12(9), 375; https://doi.org/10.3390/ijgi12090375 - 8 Sep 2023

Viewed by 1933

Abstract

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial [...] Read more.

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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