Geospatial and Temporal Semantic Analytics

Geospatial and Temporal Semantic Analytics

Matthew Perry (University of Georgia, USA), Amit Sheth (University of Georgia, USA), Ismailcem Budak Arpinar (University of Georgia, USA) and Farshad Hakimpour (University of Georgia, USA)
Copyright: © 2009 |Pages: 10
DOI: 10.4018/978-1-59140-995-3.ch021
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The amount of digital data available to researchers and knowledge workers has grown tremendously in recent years. This is especially true in the geography domain. As the amount of data grows, problems of data relevance and information overload become more severe. The use of semantics has been proposed to combat these problems (Berners-Lee et al., 2001; Egenhofer, 2002). Semantics refer to the meaning of data rather than its syntax or structure. Systems which can understand and process data at a semantic level can achieve a higher level of automation, integration, and interoperability. Applications generally use semantic technology for three basic purposes: (1) semantic integration, (2) semantic search and contextual browsing, and (3) semantic analytics and knowledge discovery (Sheth & Ramakrishnan, 2003).
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In preparation for our discussion of geospatial and temporal semantic analytics, we first review basic concepts of ontologies, for the Semantic Web and thematic analytics.


Ontologies are central to realizing semantic applications as they provide a concrete way to specify the semantics of an application domain. Ontology is classically defined as “a specification of a conceptualization” (Gruber, 1993). We can think of an ontology as consisting of two parts: a schema and instance data. The schema models a domain by defining class types (e.g., University, City) and relationship types (e.g., located_in). The schema is populated with instances of classes and relationships (e.g., The University of Georgia located_in Athens) to create facts representing knowledge of the domain. A number of ontologies describing thematic aspects of data have been developed at the Large Scale Distributed Information Systems (LSDIS) lab. Some recent examples include GlycO and ProPreO in the Bioinformatics domain (Sahoo et al., 2006) and more general-purpose ontologies such as the Semantic Web Evaluation Ontology (SWETO) (Aleman-Meza et al., 2004).

There has been significant work regarding the use of geospatial ontologies in geographic information science. Ontologies in geographic information systems (GIS) are seen as a vehicle to facilitate interoperability and to limit data integration problems both from different systems and between people and systems (Agarwal, 2005). Fonseca et al. (2002) present an architecture for an ontology-driven GIS in which ontologies describe the semantics of geographic data and act as a system integrator independent of the data model used (e.g., object vs. field). Kuhn (2001) claims that, for maximum usefulness, geo-ontologies should be designed with a focus on human activities in geographic space and thus present a method for constructing domain ontologies based on the text analysis of domain documents (e.g., German traffic code text for the car navigation domain). Kuhn and Raubal (2003) also introduce the concept of semantic reference systems, of which ontologies are a component, as a means to describe the same geographic information from varying perspectives. This includes notions of semantic transformation and projection of ontologies. These operations could potentially be used to present geographic information from different scales and granularities. Frank (2003) goes a step beyond purely spatial ontologies and argues for the inclusion of the temporal dimension by describing a multi-tier ontology with space-time as the fundamental dimension of physical reality. From a Web context, Kolas et al. (2005) outline specific types of geospatial ontologies needed for integration of GIS data and services: base geospatial ontology, feature data source ontology, geospatial service ontology, and geospatial filter ontology. The base geospatial ontology provides core geospatial knowledge vocabulary while the remaining ontologies are focused on geospatial web services.

Key Terms in this Chapter

Semantic Annotation: Identifying and marking occurrences of ontological entities and relationships in raw data (e.g., documents, images, and digital geographic data).

Semantic Geospatial Web: The application of Semantic Web concepts and technologies for the sharing and reuse of geographic data and services on the web.

Semantic Web: A framework that allows data on the web to be shared and reused across application, enterprise and community boundaries. The framework is realized through metadata annotations serialized using standard representations like RDF.

Semantic Metadata: Metadata that describe contextually relevant or domain specific information about content based on a shared metadata model (e.g., ontology).

Ontology: A specification of a conceptualization consisting of a hierarchy of class types and non-hierarchical relationships between classes.

Semantic Analytics: Analyzing, searching, and presenting information using explicit semantic relationships between known entities.

Semantic Association: A complex relationship between two resources in an RDF graph. Semantic Associations can be a path connecting the resources or two similar paths in which the resources are involved.

Spatiotemporal Thematic Context (STT Context): A specification of the type of ?-path semantic association used to connect thematic entities to geospatial footprints. It is specified using a schema-level semantic association in combination with a time interval.

Uniform Resource Identifier (URI): Strings that uniquely identify resources on the web.

Resource Description Framework (RDF): A Framework for describing resources on the web. RDF makes statements about resources consisting of a Subject, Predicate, and Object which translates to a directed, labeled graph.

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