Information given in topographic map captions or in GIS models is often insufficient to recognize interesting geographical patterns. Some prototypes of GIS have already been extended with a knowledge-base and some reasoning capabilities to support sophisticated map interpretation processes. Nevertheless, the acquisition of the necessary knowledge is still a demanding task for which machine learning techniques can be of great help. This paper presents INGENS, a prototypical GIS which integrates machine learning tools to assist users in the task of topographic map interpretation. The system can be trained to learn operational definitions of geographical objects that are not explicitly modeled in the database. INGENS has been applied to the task of Apulian map interpretation in order to discover geographic knowledge of interest to town planners.
Empowering a GIS with Inductive Learning Capabilities: The Case of INGENS
MALERBA, Donato;ESPOSITO, Floriana;LANZA, Antonietta;LISI, Francesca Alessandra;APPICE, ANNALISA
2003-01-01
Abstract
Information given in topographic map captions or in GIS models is often insufficient to recognize interesting geographical patterns. Some prototypes of GIS have already been extended with a knowledge-base and some reasoning capabilities to support sophisticated map interpretation processes. Nevertheless, the acquisition of the necessary knowledge is still a demanding task for which machine learning techniques can be of great help. This paper presents INGENS, a prototypical GIS which integrates machine learning tools to assist users in the task of topographic map interpretation. The system can be trained to learn operational definitions of geographical objects that are not explicitly modeled in the database. INGENS has been applied to the task of Apulian map interpretation in order to discover geographic knowledge of interest to town planners.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
