Framework for probabilistic geospatial ontologies Export

@article{sen_framework_2008, abstract = {Partial knowledge about geospatial categories is critical for knowledge modelling in the geospatial domain but is beyond the scope of conventional ontologies. Degree of overlaps between geospatial categories, especially those based on geospatial actions concepts and geospatial enitity concepts need to be specified in ontologies. We present an approach to encode probabilistic information in geospatial ontologies based on the BayesOWL approach. This paper presents a case study of using road network ontologies. Inferences within the probabilistic ontologies are discussed along with inferences across ontologies using common concepts of geospatial actions within each ontology. The results of machine-based mappings produced are verified with human generated mappings of concepts.}, author = {Sen, S.}, citeulike-article-id = {3015293}, citeulike-linkout-0 = {http://c4i.gmu.edu/URSW/2007/files/papers/URSW2007_T1_Sen.pdf}, journal = {INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE}, keywords = {geospatial, ontologies, probabalistic, semantic}, number = {7}, pages = {825--846}, posted-at = {2008-07-17 19:28:20}, priority = {3}, title = {Framework for probabilistic geospatial ontologies}, url = {http://c4i.gmu.edu/URSW/2007/files/papers/URSW2007_T1_Sen.pdf}, volume = {22}, year = {2008} }

TY - JOUR ID - sen_framework_2008 L3 - citeulike-article-id:3015293 N2 - Partial knowledge about geospatial categories is critical for knowledge modelling in the geospatial domain but is beyond the scope of conventional ontologies. Degree of overlaps between geospatial categories, especially those based on geospatial actions concepts and geospatial enitity concepts need to be specified in ontologies. We present an approach to encode probabilistic information in geospatial ontologies based on the BayesOWL approach. This paper presents a case study of using road network ontologies. Inferences within the probabilistic ontologies are discussed along with inferences across ontologies using common concepts of geospatial actions within each ontology. The results of machine-based mappings produced are verified with human generated mappings of concepts. IS - 7 JF - INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE EP - 846 TI - Framework for probabilistic geospatial ontologies VL - 22 SP - 825 KW - geospatial KW - ontologies KW - probabalistic KW - semantic AU - Sen, S PY - 2008/// UR - http://c4i.gmu.edu/URSW/2007/files/papers/URSW2007_T1_Sen.pdf ER -