Understanding relevance in maps through the use of knowledge graphs

Abstract

The relation that the ontological and spatial dimensions have is not always evident. To better understand this relation, a tool called SeMaptics that connects the two domains were developed. Ontological mapping allows for discrete ontologies to be projected into the spatial field. Such ontologies are regularly seen in a continuous or overlapping layered format in the spatial dimension. However, integrating both spaces results in a novel method, which adds additional perspectives when designing a map.The objective is to link and map both dimensions enabling user interactions, and allowing for proper semantic probing. This exploration contains some criteria of geographical relevance. This criteria allows for better identification of high-interest areas within any given dataset.SeMaptics implements graphDB Neo4j to accommodate a graph architecture and visualization JavaScript library D3js to render knowledge graphs on the screen. Visual representations of knowledge graphs are limited by performance as the data pool grows. In this work, an isolated administrative unit in Austria is analyzed to have a balance between semantics and data quantity. Criteria that comprise geographical relevance are over-viewed in this style of mapping to understand if it is the use relevance when exploring spatial data from a semantic viewpoint.