Datum
2022-04-20Metadata
Zur Langanzeige
Aufsatz
Knowledge cores in large formal contexts
Zusammenfassung
Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
Zitierform
In: Annals of Mathematics and Artificial Intelligence Volume 90 / Issue 6 (2022-04-20) , S. 537-567 ; eissn:1573-7470Förderhinweis
Gefördert im Rahmen des Projekts DEALZitieren
@article{doi:10.17170/kobra-202206246401,
author={Hanika, Tom and Hirth, Johannes},
title={Knowledge cores in large formal contexts},
journal={Annals of Mathematics and Artificial Intelligence},
year={2022}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2022$n2022 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/14121 3000 Hanika, Tom 3010 Hirth, Johannes 4000 Knowledge cores in large formal contexts / Hanika, Tom 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/14121=x R 4204 \$dAufsatz 4170 5550 {{Wissensmanagement}} 5550 {{Wissensbasis}} 5550 {{Formale Begriffsanalyse}} 5550 {{Verband <Mathematik>}} 7136 ##0##http://hdl.handle.net/123456789/14121
2022-09-01T09:18:25Z 2022-09-01T09:18:25Z 2022-04-20 doi:10.17170/kobra-202206246401 http://hdl.handle.net/123456789/14121 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ k-cores Bi-partite graphs formal concept analysis lattices implications knowledge base 004 Knowledge cores in large formal contexts Aufsatz Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts. open access Hanika, Tom Hirth, Johannes doi:10.1007/s10472-022-09790-6 Wissensmanagement Wissensbasis Formale Begriffsanalyse Verband <Mathematik> publishedVersion eissn:1573-7470 Issue 6 Annals of Mathematics and Artificial Intelligence 537-567 Volume 90 false
Die folgenden Lizenzbestimmungen sind mit dieser Ressource verbunden: