Measuring semantic similarity between land-cover classes for spatial analysis: an ontology hierarchy exploration approach

Resolving semantic heterogeneity is one of the major research challenges involved in many fields of study, such as, natural language processing, search engine development, document clustering, geospatial information retrieval, knowledge discovery, etc. When semantic heterogeneity is often considered as an obstacle for realizing full interoperability among diverse datasets, proper quantification of semantic similarity is another challenge to measure the extent of association between two qualitative concepts. The proposed work addresses this issue for any geospatial application where spatial land-cover distribution is crucial to model. Most of the these applications such as: prediction, change detection, land-cover classification, etc. often require to examine the land-cover distribution of the terrain. This paper presents an ontology-based approach to measure semantic similarity between spatial land-cover classes. As land-cover distribution is a qualitative information of a terrain, it is challenging to measure their extent of similarity among each other pragmatically. Here, an ontology is considered as the concept hierarchy of different land-cover classes which is built using domain experts’ knowledge. This work can be considered as the spatial extension of our earlier work presented in [1]. The similarity metric proposed in [1] is utilized here for spatial concepts. A case study with real land-cover ontology is presented to quantify the semantic similarity between every pair of land-covers with semantic hierarchy based similarity measurement (SHSM) scheme [1]. This work may facilitate quantification of semantic knowledge of the terrain for other spatial analyses as well.