Implicit, Formal, and Powerful Semantics in Geoinformation

Abstract

Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics.

An Ontology-Based Knowledge Representation Method for Typhoon Events from Chinese News Reports

“…The news reports were processed by clauses, and then the sentences were read manually to extract the news content. The concepts, relationships, and rules of typhoon events were derived from various sources, including standard specifications, laws, and regulations (e.g., the Meteorological Disaster Prevention Ordinance), professional books (e.g., Typhoon Forecast and Disaster), literature [21,35,37,39,46,47,49,50,52,68,69], official news, and statistical data materials (e.g., Tropical Cyclone Yearbook) about typhoons. For instance, the Chinese standard document 'GB/T 19201-2006' defines the level of tropical cyclones; 'GB/T 26376-2010' defines basic terms for natural disaster management; 'GB/T 28921-2012' defines the categories of natural disasters, and 'GB/T 35663-2017' defines the terms and definitions of weather conditions, meteorological elements and weather phenomena.…”

Artificial Intelligence for Multisource Geospatial Information

“…Additionally, FSOLAP is not only used to query and analyse historical data but also to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism. (x) "Implicit, Formal, and Powerful Semantics in Geoinformation" by the authors herein and our colleagues Paolo Tagliolato Acquaviva D'Aragona and Paola Carrara [13] addresses the need to identify suitable methodologies and frameworks in order to represent and mine GBD depending on their geosemantics-whose classification is often ill-defined. A meta-review of the state of the art in geosemantics is performed to pinpoint relevant "keywords" representing key concepts, challenges, methods, and technologies of the domain.…”

Enabling the Reuse of Long-Term Marine Biological Observations in Essential Variables Frameworks Through a Practical Approach

“…To overcome this issue, and to maintain semantic interoperability of such less strict schematization, it is highly recommended the use of URIs instead of simple texts for linking measurement types, values and units to internationally acknowledged controlled vocabularies and thesauri (OBIS, 2021). Please refer to Bordogna et al (2021) for an updated account of perspectives on semantics in geo-based information.…”