Approaches to Semantic Similarity Measurement for Geo‐Spatial Data: A Survey

TLDR

Semantic similarity is central to semantically enabled processing of geospatial data, underpinning retrieval, integration, and the design of GIS systems. This article surveys theories of semantic similarity measurement and reviews how psychological models of human similarity judgment can be applied to geographic information science. The authors classify existing similarity measures into geometric, feature, network, alignment, and transformational models, review each model’s notion of similarity and metric properties, and evaluate them against the requirements for measuring semantic similarity between geospatial data. The survey compares the similarity measures, offers general advice for selecting an appropriate measure, and highlights that each measure’s advantages and disadvantages determine its suitability for specific tasks.

Abstract

Abstract Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.