Ontology Patterns for Complex Topographic Feature Types

By Varanka, Dalia E. | Cartography and Geographic Information Science, April 2011 | Go to article overview

Ontology Patterns for Complex Topographic Feature Types


Varanka, Dalia E., Cartography and Geographic Information Science


Introduction

Feature types, in the sense of 'an abstraction of real world phenomena are complex when they are assemblages of multiple components that depend on each other for functional or some other meaningful purposes (International Organization for Standardization 2002). "Complex map features can be built up from simple ones" (Clarke 2001, 60). The components of a complex feature have high relevance values and strong associations and are similar in context (Ramakrishnan et al. 2005). Assemblages may be multiple occurrences of a single feature type; for example, expanses of tree area in the form of woodlands. Other complex feature types are combinations of different feature types and associated geometries, such as the control tower points, runway lines and building areas of airports.

Complex feature formation is described as spatial objects in the topology of connected coverage frameworks or the object data model (Chrisman 1997; Longley et al. 2001). In the data model approach, feature classes are grouped by shared geometric type, points, lines, or areas, and not as semantic categorizations. As geographic objects, feature type classes are stored in relational tables with properties and relations to other objects. The representation of complex features in GIS was called graphical entities (Lo and Yeung 2002). The level of generalization in the representation of complex features may be directed by function (Chaudhry et al. 2009). The representation of geospatial features can be expected to become more complex with the spread of interactive social media on the Internet to include cultural and temporal aspects, such as person identities, social interactions, or aspects of everyday life (National Geospatial-Intelligence Agency 2007).

The published literature about complex feature data includes little about the semantic concepts that relate the components together. As data, complex features are sometimes captured from satellite or aerial imagery. Complex features are difficult to define in many remote sensing data model modalities because of the blur of multi-band signatures or the overlap of features when viewed from above in aerial photographs. As a result, the visual identification of complex features in remotely sensed images requires expert interpretation and could produce false-positives. Ambiguity in complex feature representation may even be exploited for purposeful misidentification. Traditional geometric data models, such as points, lines, and areas, are well suited to represent simple, basic feature types, such as 'lake,' 'road,' or 'location point.' GIS commonly require relational tables to relate objects from dissimilar feature classes into feature complexes. Though it can be done, most geographic information systems (GIS) cannot flexibly enable combinations of geometric types such as points, lines, and areas (ESRI 2010). As a result, the representation of a complex feature is made to 'fit' a simple shape through cartographic generalization, such as representing a mine as a dot on a map, though the features are multi-dimensional.

Semantic attributes of geographic features are measured, categorized, and stored with the spatial geometry of the feature in the basic data table, but any advanced development and analysis of semantic complexity involves the skill of the GIS analyst to design and manipulate (Clarke 2001). This study examines the potential of using ontology design pattern (ODP) technology to represent complex topographic feature types in a way that details the semantic meaning of feature assemblages. Specifying data property semantics automates a component of geographic information analysis that is largely manual in most GIS. A range of GIS functions are researched to be more quickly accomplished with semantic specification. Ontology applications help in searching, querying, and retrieval (Teevan et al. 2005; Wang et al. 2007), annotation (Dubbeldam et al. 2001; Dill et al. …

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