It is difficult to share and reuse geospatial data and retrieve geospatial information because of geospatial data heterogeneity problems. Lack of semantic interoperability is one of the major problems facing GIS (Geographic Information Science/System) systems and applications today. To solve geospatial data heterogeneity problems and support geospatial information retrieval and semantic interoperability over the Web, the use of an ontology is proposed because it is a formal explicit description of concepts or meanings of words in a well-defined and unambiguous manner. Geospatial ontologies represent geospatial concepts and properties for use over the Web. OWL (Ontology Web Language) is an emerging language for defining and instantiating ontologies. OWL builds on RDF (Resource Description Framework) but adds more vocabulary for describing properties and classes. The downside of representing structured geospatial data in OWL and RDF languages is that it can result in inefficient data access. SPARQL (Simple Protocol and RDF Query Language) is recommended for general RDF query while the GeoSPARQL (Geographic Simple Protocol and RDF Query Language) protocol is proposed as an extension of SPARQL for querying geospatial data. However, the runtime cost of GeoSPARQL queries can be high due to the fine-grained nature of RDF data models. There are several challenges to using ontologies for geospatial semantic interoperability but these can be overcome through collaboration.
The increasing accessibility of geospatial data in the form of knowledge graphs, developed in alignment with the Semantic Web vision and employing Linked Data principles, is becoming a prominent feature of the Web. The multitude of available geospatial knowledge graphs demonstrates their indispensable role within the Web of Data Cloud. Such graphs serve as central nexuses, interconnecting events, people, and objects, offering an ever-growing semantic representation of the geospatial information wealth. The resulting resources and capabilities are being leveraged to utilize, consume, and capitalize on geospatial information through the strategic deployment of knowledge graphs. The geospatial graphs are stored and managed by triple stores, which are also known as RDF stores or knowledge bases. As examples, SPARQL and GeoSPARQL are semantic query languages that are used to retrieve and process knowledge graphs. Some developments and experiences in the GIScience community have demonstrated the feasibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from disparate and distributed sources. These efforts have facilitated the consumption of geospatial knowledge graphs through lightweight web applications or GIS applications.