Special Issue "Geospatial Semantic Web: Resources, Tools and Applications"

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

Deadline for manuscript submissions: 31 July 2021.

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 papers will be 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 1400 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

Published Papers (1 paper)

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Research

Article
Semantics of Voids within Data: Ignorance-Aware Machine Learning
ISPRS Int. J. Geo-Inf. 2021, 10(4), 246; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040246 - 08 Apr 2021
Viewed by 303
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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