Paper Info
Title
Constructing geo-ontologies by reification of observation data
Abstract
The semantic integration of heterogeneous, spatiotemporal information is a major challenge for achieving the vision of a multi-thematic and multi-perspective Digital Earth. The Semantic Web technology stack has been proposed to address the integration problem by knowledge representation languages and reasoning. However approaches such as the Web Ontology Languages (OWL) were developed with decidability in mind. They do not integrate well with established modeling paradigms in the geosciences that are dominated by numerical and geometric methods. Additionally, work on the Semantic Web is mostly feature-centric and a field-based view is difficult to integrate. A layer specifying the transition from observation data to classes and relations is missing. In this work we combine OWL with geometric and topological language constructs based on similarity spaces. Our approach provides three main benefits. First, class constructors can be built from a larger palette of mathematical operations based on vector algebra. Second, it affords the representation of prototype-based classes. Third, it facilitates the representation of classes derived from machine learning classifiers that utilize a multi-dimensional feature space. Instead of following a one-size-fits-all approach, our work allows one to derive contextualized OWL ontologies by reification of observation data.
Year
DOI
Venue
2011
10.1145/2093973.2094015
GIS
Keywords
Field
DocType
semantic web technology,one-size-fits-all approach,semantic integration,owl ontology,observation data,semantic web,constructing geo-ontologies,geometric method,knowledge representation language,web ontology languages,integration problem,digital earth,machine learning,observations,ontology,knowledge representation,web ontology language,feature space
Data mining,Semantic integration,Semantic Web Stack,Computer science,Reification (computer science),Theoretical computer science,OWL-S,Artificial intelligence,Social Semantic Web,Semantic Web Rule Language,Semantic similarity,Knowledge representation and reasoning,Machine learning
Conference
Citations 
PageRank 
References 
5
0.46
28
Authors
2
Name
Order
Citations
PageRank
Benjamin Adams1737.78
Krzysztof Janowicz21660105.59