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Information
Special Issues
Geo Information and Knowledge Graphs
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Geo Information and Knowledge Graphs

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (1 August 2019) | Viewed by 9465

Special Issue Editor

Dr. Francisco J. Lopez-Pellicer

E-Mail Website
Guest Editor
Advanced Information Systems laboratory (IAAA), Aragon Institute of Engineering Research (I3A), Universidad Zaragoza. c/María de Luna 1, 50018 Zaragoza, Spain
Interests: development of geospatial ontologies; vocabularies and gazetteers; discovery and indexing of geo Web resources; publication of geo information in the Web of Linked Data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the term knowledge graph (KG) has begun to be used in research, usually in close association with linked data, large-scale data analytics, and cloud computing. Today, KGs augment the majority of search engines, recommendation systems, personal assistants, and cyber-infrastructures, supporting a wide variety of user-facing activities and improving behind-the-scenes machine learning. Geospatial information plays a vital role in KGs in many application scenarios, such as smart infrastructures, environmental management, navigation, logistics, and tourism. However, despite improving implementations, traditional geographic information systems (GIS) still outperform KGs technologies in functionality, efficiency, and scalability, as they have expert knowledge of what is special about spatial content. How we can solve this gap?

This Special Issue on “Geo Information in Knowledge Graphs” aims to focus on the emerging need for the effective and efficient production, management, and utilization of geospatial information within KGs. Authors should submit papers describing significant, original, and unpublished work. Possible topics include but are not limited to the following:

  • Geospatial linked data vocabularies and standards used in knowledge graphs;
  • Extraction/transformation of geospatial data sources into knowledge graphs;
  • Integration techniques for geospatial knowledge graphs;
  • Enrichment, quality, and evolution of knowledge graphs with geospatial information;
  • Machine learning improving geospatial knowledge graphs processing;
  • Algorithms and tools for large-scale, scalable geospatial knowledge graph management;
  • Efficient indexing and querying of geospatial knowledge graphs;
  • Geospatial-specific querying and/or reasoning on knowledge graphs;
  • Ranking techniques on querying geospatial knowledge graphs;
  • Benchmarking of applications powered by geospatial knowledge graphs;
  • Novel approaches for browsing/authoring/querying geospatial knowledge graphs;
  • Survey of real-world applications using geospatial knowledge graphs;
  • Survey of tools/libraries/frameworks for geospatial knowledge graphs.
Dr. Francisco J. Lopez-Pellicer
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. 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 1600 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.

Published Papers (1 paper)

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Research

19 pages, 4809 KiB  
Article
Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data
by Stanislav Ronzhin, Erwin Folmer, Pano Maria, Marco Brattinga, Wouter Beek, Rob Lemmens and Rein van’t Veer
Information 2019, 10(10), 310; https://doi.org/10.3390/info10100310 - 09 Oct 2019
Cited by 18 | Viewed by 9051
Abstract
After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, [...] Read more.
After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots. Full article
(This article belongs to the Special Issue Geo Information and Knowledge Graphs)
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