TITLE

Multi-view Augmented Concept in Support of Geospatial Data Retrieval

AUTHOR(S)
Bakillah, Mohamed; Mostafavi, Mir Abolfazl; Brodeur, Jean
PUB. DATE
January 2012
SOURCE
Journal of Earth Science & Engineering;Jan2012, Vol. 2 Issue 1, p51
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Retrieving relevant geospatial data has become increasingly critical because of the growing volume of geospatial data made available to various users through distributed environment. In this context, semantics of geospatial data is also critical, because it allows the user to understand the meaning of shared data, and the system to automatically identify and resolve semantic heterogeneity of data. However, geospatial data often lack explicit semantics, which can lead to low performance of search engines, and misinterpretation or misuse of retrieved data. In particular, the complexity of geospatial data increases the importance of explicit semantics; we have identified a lack of semantics with respect to contexts of concepts, spatiotemporal semantics, and dependencies between concepts' features. A solution to poor semantics of geospatial data is semantic enrichment. In this paper, we propose an approach to geospatial data retrieval based on enrichment of geospatial data semantics, which contributes to solving the identified retrieval problems caused by lack of semantics. The proposed approach is based on a semantically augmented representation of the concept. A semantic enrichment system generates enriched concepts with semantic reasoning engines and data mining techniques. Then, a semantic mapping system determines the semantic correspondences between the users' queries and the enriched concepts of databases' ontologies. More specifically, this retrieval system is able to compute context-dependent semantic mappings; to consider spatiotemporal semantics when comparing spatiotemporal features of concepts; and to use dependencies between features to identify "missing mappings" that could not be detected otherwise. As a result, and as illustrated in a case study, the identification of relevant data sets by the retrieval system is improved, and the system is able to point out semantic heterogeneity problems that could lead to misinterpretation of data.
ACCESSION #
76285312

 

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