Geospatial Ontology Development and Semantic Analytics Export

@article{citeulike:742965, abstract = {Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO-GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi-automatically tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.}, address = {Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Department of Geography University of Georgia; Department of Geography University of Georgia; Department of Geography Ohio State University}, author = {Arpinar, I. Budak and Sheth, Amit and Ramakrishnan, Cartic and Usery, E. Lynn and Azami, Molly and Kwan, Mei-Po}, citeulike-article-id = {742965}, citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9671.2006.01012.x}, citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1467-9671.2006.01012.x}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/bpl/tgis/2006/00000010/00000004/art00004}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/118566629/ABSTRACT}, doi = {10.1111/j.1467-9671.2006.01012.x}, issn = {1467-9671}, journal = {Transactions in GIS}, keywords = {geographicalmetadata, ontology, ontologyexploitation}, month = {July}, number = {4}, pages = {551--575}, posted-at = {2008-06-09 16:28:57}, priority = {2}, publisher = {Blackwell Publishing}, title = {Geospatial Ontology Development and Semantic Analytics}, url = {http://dx.doi.org/10.1111/j.1467-9671.2006.01012.x}, volume = {10}, year = {2006} }

TY - JOUR ID - citeulike:742965 L3 - citeulike-article-id:742965 N2 - Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO-GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi-automatically tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP. IS - 4 JF - Transactions in GIS SN - 1467-9671 AD - Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Large Scale Distributed Information Systems Lab Computer Science Department University of Georgia; Department of Geography University of Georgia; Department of Geography University of Georgia; Department of Geography Ohio State University EP - 575 TI - Geospatial Ontology Development and Semantic Analytics PB - Blackwell Publishing VL - 10 SP - 551 KW - geographicalmetadata KW - ontology KW - ontologyexploitation AU - Arpinar, Budak AU - Sheth, Amit AU - Ramakrishnan, Cartic AU - Usery, Lynn AU - Azami, Molly AU - Kwan, Mei-Po PY - 2006/07// UR - http://dx.doi.org/10.1111/j.1467-9671.2006.01012.x ER -