A New Method for the Semantic Integration of Multiple OWL Ontologies using Alignments
October 05, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Inès Osman
arXiv ID
1810.02869
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This work is done as part of a master's thesis project. The goal is to integrate two or more ontologies (of the same or close domains) in a new consistent and coherent OWL ontology to insure semantic interoperability between them. To do this, we have chosen to create a bridge ontology that includes all source ontologies and their bridging axioms in a customized way. In addition, we introduced a new criterion for obtaining an ontology of better quality (having the minimum of semantic/logical conflicts). We have also proposed new terminology and definitions that clarify the unclear and misplaced "integration" and "merging" notions that are randomly used in state-of-the-art works. Finally, we tested and evaluated our OIA2R tool using ontologies and reference alignments of the OAEI campaign. It turned out that it is generic, efficient and powerful enough.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted