The map as knowledge base

Varanka, Dalia E.; Usery, E. Lynn

doi:10.1080/23729333.2017.1421004

Cited by 26 publications

(15 citation statements)

References 28 publications

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“…For example, the GeoSPARQL (Battle and Kolas, 2011) has incorporated spatial topology and the Time Ontology (Cox and Little, 2020) has included temporal topology. Those endeavors together have laid the foundation for more innovative approaches of online spatial data analysis (Varanka and Usery, 2018).…”

Section: Knowledge Graphs and Digital Map Analysismentioning

confidence: 99%

Knowledge graph construction and application in geosciences: A review

Ma¹

2022

Preprint

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Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts rele-vant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geoscience KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar stud-ies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.

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Section: Knowledge Graphs and Digital Map Analysismentioning

confidence: 99%

Knowledge graph construction and application in geosciences: A review

Ma¹

2022

Preprint

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“…However, these works were mostly considering the development of a knowledge base to support map generalization. More recently, Varanka and Usery [76] consider the map itself as a knowledge base and propose an ontology that would not only include data and design concepts but also semantic and logical knowledge that are also embedded in the map. They also present an architecture based on a triplestore where map elements can be retrieved with SPARQL and GeoSPARQL queries.…”

Section: Domain Task and Application Ontologiesmentioning

confidence: 99%

“…On one hand, ontologies were built to formalize expert knowledge in a specific area to provide unambiguous concepts and rules. For example, landform ontologies such as [63,64] propose concepts to structure landforms in lattices while [76] provides a formalization of existing rules for contour maps in first order logic. In such approaches, ontologies are used to constrain the interpretation of the different concepts.…”

Section: Modeling Vs Encodingmentioning

confidence: 99%

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Kokla

Guilbert

2020

IJGI

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The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.

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“…A basic principle of top-down design and bottom-up development of ontology is used in the stage of design and development of GeoDataOnt (Kalbasi, Janowicz, Reitsma, Boerboom, & Alesheikh, 2014;Varanka & Usery, 2018). In the previous section, GeoDataOnt has been divided into three compound modules: essential ontology, morphology ontology, and provenance ontology from the perspective of content, which is consistent with the characteristics hierarchy of geospatial data.…”

Section: Detailed Design and Implementation Of Geodataontmentioning

confidence: 99%

Geospatial data ontology: the semantic foundation of geospatial data integration and sharing

Sun

Zhu

Pan³

et al. 2019

Big Earth Data

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Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science. However, the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing. Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines. Thus, many efforts have been made to explore ontology-based geospatial data integration and sharing. However, there is a lack of a specialized ontology that would provide a unified description for geospatial data. In this paper, with a focus on the characteristics of geospatial data, we propose a unified framework for geospatial data ontology, denoted GeoDataOnt, to establish a semantic foundation for geospatial data integration and sharing. First, we provide a characteristics hierarchy of geospatial data. Next, we analyze the semantic problems for each characteristic of geospatial data. Subsequently, we propose the general framework of GeoDataOnt, targeting these problems according to the characteristics of geospatial data. GeoDataOnt is then divided into multiple modules, and we show a detailed design and implementation for each module. Key limitations and challenges of GeoDataOnt are identified, and broad applications of GeoDataOnt are discussed.

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The map as knowledge base

Cited by 26 publications

References 28 publications

Knowledge graph construction and application in geosciences: A review

Knowledge graph construction and application in geosciences: A review

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Geospatial data ontology: the semantic foundation of geospatial data integration and sharing

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