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IJGI
Special Issues
Geospatial Semantic Web: Resources, Tools and Applications
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Geospatial Semantic Web: Resources, Tools and Applications

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 11588

Special Issue Editor

Dr. Dana Dannélls

E-Mail Website
Guest Editor
Språkbanken Text, Department of Swedish, University of Gothenburg, Box 200, SE-405 30 Gothenburg, Sweden
Interests: natural language processing; multilingual natural language generation; lexical semantics; semantic web; knowledge representation; digital humanities

Special Issue Information

Dear Colleagues,

With the advances of Artificial Intelligence (AI) and the rapid development of Semantic Web technologies, there is an increasing need for advanced resources and tools for improving geospatial applications. To better understand the strengths and weaknesses of today’s technologies and thereby gain knowledge about how to improve intelligence applications, we need to learn more about their potentials and shortcomings.

This Special Issue aims to highlight existing challenges in gathering and analyzing available data, both structured and unstructured, and propose solutions to address those. It further aims to explore methodological issues that are involved in developing tools and applications for accessing available data.

Relevant topics to this issue include but are not limited to:

  • Geospatial knowledge graphs: construction, representation, alignment;
  • Semantic web and natural language processing resources: development, modeling, integration;
  • Geospatial text analysis: semantic parsing, indexing, toponym recognition, information extraction;
  • Machine and deep learning methods of geospatial;
  • Applications: summarization, visualization, question answering, information retrieval, image analysis;
  • Evaluation of geospatial methods and applications. 

Dr. Dana Dannélls
Guest Editor

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

  • artificial intelligence
  • machine learning
  • knowledge graph
  • geographic information science
  • ontology
  • language technology resources
  • information technology
  • Natural Language Processing

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Published Papers (3 papers)

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Research

27 pages, 2894 KiB  
Article
Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs
by Erum Haris and Keng Hoon Gan
ISPRS Int. J. Geo-Inf. 2021, 10(10), 710; https://doi.org/10.3390/ijgi10100710 - 18 Oct 2021
Cited by 10 | Viewed by 3801
Abstract
Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing [...] Read more.
Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing works have studied tourist places, but results lack a coherent outline and visualization of the semantic knowledge associated with tourist attractions. Hence, this work proposes place semantics extraction based on a fusion of content analysis and natural language processing (NLP) techniques. A weighted-sum equation model is then employed to construct a points of interest graph (POI graph) that integrates extracted semantics with conventional frequency-based weighting of tourist spots and routes. The framework offers determination and visualization of massive blog text in a comprehensible manner to facilitate individuals in travel decision-making as well as tourism managers to devise effective destination planning and management strategies. Full article
(This article belongs to the Special Issue Geospatial Semantic Web: Resources, Tools and Applications)
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12 pages, 1115 KiB  
Communication
Semantic-Linked Data Ontologies for Indoor Navigation System in Response to COVID-19
by Abdullah Alamri
ISPRS Int. J. Geo-Inf. 2021, 10(9), 607; https://doi.org/10.3390/ijgi10090607 - 14 Sep 2021
Cited by 9 | Viewed by 3631
Abstract
Indoor navigation has become more important these days due to the current situation worldwide in the aftermath of the outbreak of the COVID-19 pandemic, posing an unparalleled threat amounting to a humanitarian crisis on a global scale. Indoor navigation employs a variety of [...] Read more.
Indoor navigation has become more important these days due to the current situation worldwide in the aftermath of the outbreak of the COVID-19 pandemic, posing an unparalleled threat amounting to a humanitarian crisis on a global scale. Indoor navigation employs a variety of technologies, including Wi-Fi, Bluetooth, and RFID. Support for these technologies requires accurate information and appropriate processing and modeling to help and direct users of the optimal route to desired destinations and to monitor crowd density in order to maintain social distancing. This research will present a semantic indoor ontology model for indoor navigation and the reduction of human density in indoor space to ensure social distancing and prevent transmission. The proposed system is based on semantic representations of the components of navigation paths which, in turn, enable reasoning functionality. Despite the system’s complexity, the evaluation revealed that it functions well. Full article
(This article belongs to the Special Issue Geospatial Semantic Web: Resources, Tools and Applications)
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33 pages, 4458 KiB  
Article
Semantics of Voids within Data: Ignorance-Aware Machine Learning
by Vagan Terziyan and Anton Nikulin
ISPRS Int. J. Geo-Inf. 2021, 10(4), 246; https://doi.org/10.3390/ijgi10040246 - 8 Apr 2021
Cited by 6 | Viewed by 3246
Abstract
Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to [...] Read more.
Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery. Full article
(This article belongs to the Special Issue Geospatial Semantic Web: Resources, Tools and Applications)
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