@conference {888,
	title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids},
	booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
	year = {2021},
	pages = {305{\textendash}308},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {<p>We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.</p>
},
	keywords = {Hierarchical Grids, Knowledge Graphs, RCC5},
	isbn = {9781450386647},
	doi = {10.1145/3474717.3483965},
	url = {https://doi.org/10.1145/3474717.3483965},
	author = {Zalewski, Joseph and Hitzler, Pascal and Janowicz, Krzysztof}
}
@article {864,
	title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)},
	year = {2021},
	abstract = {<p>We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.</p>
},
	keywords = {Hierarchical Grids, Knowledge Graphs, RCC5},
	author = {Joseph Zalewski and Pascal Hitzler and Krzysztof Janowicz}
}
