Publications

@inproceedings{mccutchan18:1:1[TUW-277897],

title = {Geospatial Semantics for Adaptive Interaction},

author = {Marvin McCutchan and Ioannis Giannopoulos},

editor = {Werner Kuhn and Karen Kemp and others},

year  = {2018},

date = {2018-01-01},

booktitle = {GIScience 2018 - Workshop on Core Computations on Spatial Information},

pages = {1:1--1:4},

abstract = {This work presents a concept for adaptive interaction dialogues which are based on geospatial semantics and machine learning. The proposed system should enable users to efficiently and effectively interact with their surrounding environment. Through this adaptive interaction dialogues the users should be able to ask relevant questions in a more natural way.},

note = {Vortrag: GIScience 2018 - Workshop on Core Computations on Spatial Information, Melbourne, Australien; 2018-08-28},

keywords = {Geospatial semantics, Linked Data, Machine Learning, spatial prediction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mccutchan18:45:1[TUW-271425],

title = {Geospatial Semantics for Spatial Prediction},

author = {Marvin McCutchan and Ioannis Giannopoulos},

editor = {Stephan Winter and Amy Griffin and Monika Sester},

url = {https://publik.tuwien.ac.at/files/publik_271425.pdf},

doi = {10.4230/LIPIcs.GIScience.2018.45},

isbn = {978-3-95977-083-5},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings 10th International Conference on Geographic Information Science (GIScience 2018)},

pages = {45:1--45:6},

publisher = {LIPICS},

address = {114},

abstract = {In this paper the potential of geospatial semantics for spatial predictions is explored. Therefore data from the LinkedGeoData platform is used to predict landcover classes described by the CORINE dataset. Geo-objects obtained from LinkedGeoData are described by an OWL ontology, which is utilized for the purpose of spatial prediction within this paper. This prediction is based on an association analysis which computes the collocations between the landcover classes and the semantically described geo-objects. The paper provides an analysis of the learned association rules and finally concludes with a discussion on the promising potential of geospatial semantics for spatial predictions, as well as potentially fruitful future research within this domain.},

note = {Vortrag: 10th International Conference on Geographic Information Science (GIScience 2018), Melbourne; 2018-08-28 -- 2018-08-31},

keywords = {Geospatial semantics, Linked Data, Machine Learning, spatial prediction},

pubstate = {published},

tppubtype = {inproceedings}

}