Hierarchical Semantic Correspondence Analysis on Feature Classes between Two Geospatial Datasets Using a Graph Embedding Method

Abstract

A method to find corresponding feature class pairs, including hierarchical M:N pairs between two geospatial datasets is proposed. Applying an overlapping analysis to the object sets within the feature classes, the similarities of the feature classes are estimated and projected onto a lower-dimensional vector space after applying the graph embedding method. In this space, conventional mathematical tools-agglomerative hierarchical clustering in this study-could be used to analyze semantic correspondences between the datasets and identify their hierarchical M:N corresponding pairs. The proposed method was applied to two cadastral parcel datasets; one for latest land-use records in an urban information system, and the other, for original land-use categories in the Korea land information system. To quantitatively assess identified feature pairs, F-measures for each pair are presented. The results showed that it was possible to find various semantic correspondences of the feature classes and infer regional land development characteristics.to identify the corresponding feature class pairs, which represent the same geographic entities or phenomena. While these two steps are related to model-(or dataset)-oriented analysis, the remaining steps correspond to the object-oriented analysis used to identify matching object pairs, and then, to address the geometric discrepancies between them.While these two steps are related to model-(or dataset)-oriented analysis, the remaining steps correspond to the object-oriented analysis used to identify matching object pairs, and then, to address the geometric discrepancies between them. Figure 1. Conceptual framework of a general process to integrate two geospatial databases (adapted from [5]).When the geospatial datasets to be integrated originate from a similar domain, a simple comparison of feature class names would provide the desired results for the semantic filter step. However, in the case when they are from different domains, the names can be the same or similar, even though the feature classes represent substantially different real-world entities or phenomena. Moreover, the corresponding relations may vary from 1:1 to 1:N or M:N. In these cases, detailed data specifications of the datasets to be compared are necessary. However, most of the datasets do not provide such information [6].To address this problem, various object-based analysis techniques have been proposed. These techniques use matching objects between two datasets to identify corresponding feature classes. They assume that, if spatial objects of a certain feature class in one geospatial dataset correspond to spatial objects in another feature class in the other dataset with a high probability, there is high semantic similarity between the two feature classes [7]. Uitermark et al. [2] extended this method by introducing taxonomical and partonomical relationships of feature classes within each dataset, so that relations of feature classes between datasets, as well as within each dataset, can be obtained. Similarly, aut...

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

“…Several models were shown to be competitive with human-based feature norms across all three investigations conducted by [34]. In contrast, COALS was the most reliably determined to be on par with the feature-based standards when it came to recreating semantic classes across datasets.…”