Abstract
To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing these approaches in terms of semantic similarity assessment. In this paper, we propose an ontological approach to assess the similarity of the domain of LULC classification systems and standards. We develop domain ontologies to explicitly define the descriptions and codes of different LULC classification systems and standards as semantic information, and formally organize this semantic information as rules for logical reasoning. Then, we utilize a Bayes algorithm to create a conditional probabilistic model for computing the semantic similarity of terms in two separate LULC land cover classification systems. The experiment shows that semantic similarity can be effectively measured by integrating a probabilistic model based on the content of ontology.
Highlights
Mapping land cover (LULC) provides important support for representing the historical trajectory and present situation of earth observation [1,2], land management [3], pattern analysis [4], settlement monitoring [5], landscape planning [6], etc
Tens of LULC classification systems have been developed by different nations, institutes, and organizations, such as the NLCD1992 and the NLCD2006 developed by USGS (U.S Geological Survey), the C-CAP developed by NOAA (National Oceanic and Atmospheric Administration), the Land Cover classification systems, legends developed by the UN (United Nations), and Chinese Current Land Use Classification
To accurately assess the semantics similarity of LULC classification systems with a limited amount of text information, we propose an ontology-enhanced probabilistic approach to enhance the semantic similarity measuring regarding the domain of LULC classification systems and standards
Summary
Mapping land cover (LULC) provides important support for representing the historical trajectory and present situation of earth observation [1,2], land management [3], pattern analysis [4], settlement monitoring [5], landscape planning [6], etc. Some previous works have used metadata to define the characteristics of the relationship of LULC types; the work proposed by Comber, Fisher, and Wadsworth [10] claimed that the metadata could not explicitly describe the meaning of LULC information To deal with this challenge, a number of semantic harmonization regarding LULC focuses on statistical learning-based semantic similarity assessment, such as conceptual spaces [11], semantic metrics [12], integrating post-classification and semantic metrics [13], regression integrated correlation matrix [14], etc. The remainder of this paper is organized as follows: Section 2 discusses the works relevant to ontology-based semantic similarity assessment; Section 3 presents our proposed methods for measuring semantic similarity, which includes an on-. Intrinsic IC computation methods can derive knowledge from ontology without the support of massive external information, the hierarchical taxonomy in an ontology might be very complex for this method
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