Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The categorization and conceptualization of geographic features is fundamental to cartography,
geographic information retrieval, routing applications, spatial decision support
and data sharing in general. However, there is no standard conceptualization of
the world. Humans conceptualize features based on numerous factors including cultural
background, knowledge, motivation and particularly space and time. Thus, geographic
features are prone to multiple, context-dependent conceptualizations reflecting local
conditions. This creates semantic heterogeneity and undermines interoperability. Standardization
of a shared definition is often employed to overcome semantic heterogeneity.
However, this approach loses important local diversity in feature conceptualizations and
may result in feature definitions which are too broad or too specific. This work proposes
the use of microtheories in Spatial Data Infrastructures, such as INSPIRE, to account
for diversity of local conceptualizations while maintaining interoperability at a global
level. It introduces a novel method of structuring microtheories based on space and
time, represented by administrative boundaries, to reflect variations in feature conceptualization.
A bottom-up approach, based on non-standard inference, is used to create
an appropriate global-level feature definition from the local definitions. Conceptualizations
of rivers, forests and estuaries throughout Europe are used to demonstrate how
the approach can improve the INSPIRE data model and ease its adoption by European
member states
