01868nas a2200145   4500008004100000022001400041245008700055210006900142260000700211520140200218100001801620700002101638700002001659856004301679       2022                        eng d  a0955-792X00a{An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas}0 aefficient algorithm for reasoning over OWL EL ontologies with no  c053 a<p>{Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$, which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}^\\{++\\}\\}\\$, which does not require a normal form transformation.}</p>
1 aCarral, David1 aZalewski, Joseph1 aHitzler, Pascal  uhttps://doi.org/10.1093/logcom/exac03201566nas a2200193   4500008004100000020001800041245007400059210006900133260005900202300001400261520093500275653002301210653002101233653000901254100002101263700002001284700002401304856004401328       2021                        eng d  a978145038664700aSemantic Compression with Region Calculi in Nested Hierarchical Grids0 aSemantic Compression with Region Calculi in Nested Hierarchical   aNew York, NY, USAbAssociation for Computing Machinery  a305–3083 a<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>
10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof  uhttps://doi.org/10.1145/3474717.3483965