Semantic Similarity Models for Geo-spatial Information System

Abstract

similarity measurement is the practice of estimating the relatedness of the concept based on the likeness of their meaning or their semantic content. Today's increasing interest on the geospatial information system, leads to development of the query system which needs to provide efficient geo-spatial information retrieval. The properties and spatial relations between the geo-spatial concepts must be taken into account for retrieving geo-spatial information efficiently. This paper provides the survey on various models such as geometric model, network model, transformation model, hybrid model, etc, for discovering the semantic similarity between the geo-spatial concepts. It also focuses the drawbacks of each semantic similarity model and depicts how the hybrid model works well when compared with other models in retrieving the Geo-spatial information efficiently. The main objective of this paper is to propose Hybrid semantic similarity model using Manhattan distance and by considering contexts of the Geo-spatial concepts. The Manhattan distance method is used to estimate the semantic distance between the Geo-spatial concepts and to retrieve the Geo-spatial information efficiently. The position of the locations is identified by using the map similarity.