A System for Aligning Geographical Entities from Large Heterogeneous Sources

Aligning points of interest (POIs) from heterogeneous geographical data sources is an important task that helps extend map data with information from different datasets. This task poses several challenges, including differences in type hierarchies, labels (different formats, languages, and levels of detail), and deviations in the coordinates. Scalability is another major issue, as global-scale datasets may have tens or hundreds of millions of entities. In this paper, we propose the GeographicaL Entities AligNment (GLEAN) system for efficiently matching large geographical datasets based on spatial partitioning with an adaptable margin. In particular, we introduce a text similarity measure based on the local-context relevance of tokens used in combination with sentence embeddings. We then come up with a scalable type embedding model. Finally, we demonstrate that our proposed system can efficiently handle the alignment of large datasets while improving the quality of alignments using the proposed entity similarity measure

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes

A Temporal Approach to Defining Place Types based on User-Contributed Geosocial Content

Place is one of the foundational concepts on which the field of Geography has been built. Traditionally, GIScience research into place has been approached from a spatial perspective. While space is an integral feature of place, it represents only a single dimension (or a combination of three dimensions to be exact), in the complex, multidimensional concept that is place. Though existing research has shown that both spatial and thematic dimensions are valuable, time has historically been under-utilized in its ability to describe and define places and their types. The recent availability and access to user-generated geosocial content has allowed for a much deeper investigation of the temporal dimension of place. Multi-resolution temporal signatures are constructed based on these data permitting both place instances and place types to be compared through a robust set of (dis)similarity measures. The primary contribution of this work lies in demonstrating how places are defined through a better understanding of temporal user behavior. Furthermore, the results of this research present the argument that the temporal dimension is the most indicative placial dimension for classifying places by type