Knowledge representation in the semantic web for Earth and environmental terminology (SWEET)

By Robert G. Raskin, Michael J. Pan. Computational Geosciences, 2005

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geography, Planning and Development, Information Systems

View Paper: https://www.semanticscholar.org/paper/adbb2bd4f03c46f4d2ba2ae5071235dc2b162511

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. C. Mungall
2. Suzanna Lewis
3. Bilal Abu-Salih
4. Michael F. Goodchild
5. Germain Forestier
6. Barry Smith
7. Max J. Egenhofer

In Geography, Planning and Development

1. Bilal Abu-Salih
2. Michael F. Goodchild
3. Brent J. Hecht
4. Max J. Egenhofer

In Information Systems

1. Bilal Abu-Salih

Citations in the corpus

• Domain-specific Knowledge Graphs: A survey (2020, background): “Applying semantic web technologies and ontologies in natural sciences has proven successful leveraging the formal knowledge representation and the semantic web languages that can model rich and complex knowledge of natural sciences [110-114].”
• Coastal image interpretation using background knowledge and semantics (2013, background): “More recently, [16] developed a collection of ontologies using the web ontology language (OWL) that include both orthogonal classes (space, time, Earth realms, physical quantities, etc.”
• The environment ontology: contextualising biological and biomedical entities (2013, background): “In developing ENVO, we recognized the many existing resources which address, among other entities, environment-types [13-16] and were motivated by the value of unifying such resources in a foundational, or building block, ontology developed within a federated framework and exclusively concerned with”
• OBIS-SEAMAP: The World Data Center for Marine Mammal, Sea Bird, and Sea Turtle Distributions (2009, background): “The environmental modeling field is experiencing rapid advances in data exchange standards coincident with the emergence of enabling technologies (Argent, 2004; Frehner and Brandli, 2006; Raskin and Pan, 2005).”
• Geospatial Cyberinfrastructure: Past, present and future (2010, background): “Geospatial knowledge refers to a shared understanding of geospatial terms and the collective, expert intelligence it makes available (Raskin & Pan, 2005).”