Deeply Integrating Linked Data with Geographic Information Systems

The realization that knowledge often forms a densely interconnected
graph has fueled the development of graph databases, Web-scale
knowledge graphs, novel visualization and query paradigms, as well as
new machine learning methods tailored to graphs. While Linked Data has
found its way into everyday applications such as search engines and
question answering systems, there is a growing disconnect between the
classical ways in which GIS are still used today and the open-ended,
exploratory approaches used to retrieve and consume data from
knowledge graphs. In this work, we conceptualize and prototypically
implement a Linked Data connector framework as a set of toolboxes for
Esri's ArcGIS to close this gap and enable the retrieval, integration, and
analysis of Linked Data from within geographic information systems. We
discuss how to connect to Linked Data endpoints, how to use ontologies
to probe data and derive appropriate GIS representations on-the-fly,
how to make use of reasoning, how to derive data that is ready for
spatial analysis out of RDF triples, and, most importantly, how to utilize
the link structure of Linked Data to enable analysis. The proposed
framework can also be regarded as the first step towards a guided
geographic QA system over geographic KGs.