Geospatial Semantics

Geospatial semantics is a broad field that involves a variety of research areas. The term semantics refers to the meaning of things, and is in contrast with the term syntactics. Accordingly, studies on geospatial semantics usually focus on understanding the meaning of geographic entities as well as their counterparts in the cognitive and digital world, such as cognitive geographic concepts and digital gazetteers. Geospatial semantics can also facilitate the design of geographic information systems (GIS) by enhancing the interoperability of distributed systems and developing more intelligent interfaces for user interactions. During the past years, a lot of research has been conducted, approaching geospatial semantics from different perspectives, using a variety of methods, and targeting different problems. Meanwhile, the arrival of big geo data, especially the large amount of unstructured text data on the Web, and the fast development of natural language processing methods enable new research directions in geospatial semantics. This chapter, therefore, provides a systematic review on the existing geospatial semantic research. Six major research areas are identified and discussed, including semantic interoperability, digital gazetteers, geographic information retrieval, geospatial Semantic Web, place semantics, and cognitive geographic concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova, and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information Systems, Elsevier. Oxford, U

GEOINTERPRET: AN ONTOLOGICAL ENGINEERING METHODOLOGY FOR AUTOMATED INTERPRETATION OF GEOSPATIAL QUERIES

Despite advances in GIS technology, solving geospatial problems using current GIS platforms involves complex tasks requiring specialized skills and knowledge that are attainable through formal training and experience in implementing GIS projects. These requisite skills and knowledge include: understanding domain-specific geospatial problems; understanding GIS representation of real-world objects, concepts, and activities; knowing how to identify, locate, retrieve, and integrate geospatial data sets into GIS projects; knowing specific geoprocessing capabilities available on specific GIS platforms; and skills in utilizing geoprocessing tools in GIS with appropriate data sets to solve problems effectively and efficiently. Users interested in solving application-domain problems often lack such skills and knowledge and resort to GIS experts (this is especially true for applications dealing with diverse geospatial data sets and complex problems). Therefore, there is a gap between users' knowledge about geoprocessing and GIS tools and the GIS knowledge and skills needed to solve geospatial problems. To fill this gap, a new approach that automates the tasks involved in geospatial problem solving is needed. Of these tasks, the most important is geospatial query (usually expressed in application-specific concepts and terminologies) interpretation and mapping to geoprocessing operations implementable by GIS. The goal of this research is to develop an ontological engineering methodology, called GeoInterpret, to automate the task of geospatial query interpretation and mapping. This methodology encompasses: a conceptualization of geospatial queries; a multiple-ontology approach for representing knowledge needed to solve geospatial queries; a set of techniques for mapping elements between different ontologies; and a set of algorithms for geospatial query interpretation, mapping, and geoprocessing workflow composition. A proof of concept was developed to demonstrate the working of GeoInterpret

Semi-Automatic Ontology Alignment for Geospatial Data Integration

Abstract. In geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the local databases. Once the alignment between the global ontology and each local ontology is established, users can potentially query hundreds of databases using a single query that hides the underlying heterogeneities. Using our approach, querying can be easily extended to a new data source by aligning a local ontology with the global one. For this purpose, we have designed and implemented a tool to align ontologies. The output of this tool is a set of mappings between concepts, which will be used to produce the queries to the local databases once a query is formulated on the global ontology. To facilitate the user’s task, we propose semi-automatic methods for propagating such mappings along the ontologies. In this paper, we present the principles behind our propagation method, the implementation of the tool, and we conclude with a discussion of interesting cases and proposed solutions.

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton