Linking Geographic Vocabularies through WordNet

TL;DR

This paper presents Voc2WordNet, an unsupervised method that uses WordNet as a semantic hub to improve the integration of heterogeneous geospatial vocabularies in linked open data, demonstrating high accuracy.

Contribution

It introduces Voc2WordNet, a novel unsupervised technique for mapping geographic vocabularies to WordNet, enhancing semantic integration in geospatial linked data.

Findings

High precision and recall in aligning vocabularies

Effective integration with OpenStreetMap and GeoNames

Demonstrates the utility of WordNet as a semantic hub

Abstract

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall.