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consistency criteria for revision states and introduce the notion of revision closure, based on which the | consistency criteria for revision states and introduce the notion of revision closure, based on which the | ||
revision of ontologies is partially automatized. Additionally, we propose a notion of axiom impact which is used to determine a beneficial order of axiom evaluation in order to further increase the effectiveness of ontology revision. Finally, we develop the notion of decision spaces, which are structures for calculating and updating the revision closure and axiom impact. The use of decision spaces saves on average 75% of the costly reasoning operations during a revision. | revision of ontologies is partially automatized. Additionally, we propose a notion of axiom impact which is used to determine a beneficial order of axiom evaluation in order to further increase the effectiveness of ontology revision. Finally, we develop the notion of decision spaces, which are structures for calculating and updating the revision closure and axiom impact. The use of decision spaces saves on average 75% of the costly reasoning operations during a revision. | ||
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Aktuelle Version vom 17. Mai 2011, 07:26 Uhr
Reasoning-Supported Interactive Revision of Knowledge Bases
Reasoning-Supported Interactive Revision of Knowledge Bases
Published: 2011
Juli
Buchtitel: Proceedings of the 22nd International Joint Conference on Artificial Intelligence
Verlag: IJCAI 2011
Referierte Veröffentlichung
Note: to appear.
BibTeX
Kurzfassung
Quality control is an essential task within ontology development projects especially when the knowledge formalization is partially automatized. In this paper, we propose a reasoning-based, interactive approach to support the revision of formalized knowledge. We state
consistency criteria for revision states and introduce the notion of revision closure, based on which the
revision of ontologies is partially automatized. Additionally, we propose a notion of axiom impact which is used to determine a beneficial order of axiom evaluation in order to further increase the effectiveness of ontology revision. Finally, we develop the notion of decision spaces, which are structures for calculating and updating the revision closure and axiom impact. The use of decision spaces saves on average 75% of the costly reasoning operations during a revision.