Grammar To Graph, an approach for semantic transformation of annotations to triples

Linguistic representation of geographic knowledge is semantically complex and particularly challenging when employing geographic information technology to automate interpreted analysis dealing with unstructured knowledge. This study describes an approach called GrammarToGraph (G2G) that applies dependency grammar rules through natural language processing to transform annotation data into structured geospatial semantic graph triples. This approach offers data handling advantages that include reducing string annotation storage needs, improving the logical specification of relations between objects, and providing reusable classes and properties that support graph queries and logic inference.