Object-Oriented Knowledge Extraction using Universal Exploiters
September 08, 2017 Β· Declared Dead Β· π International Conference on Computer Science and Information Technologies
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dmytro Terletskyi
arXiv ID
1709.02642
Category
cs.AI: Artificial Intelligence
Citations
3
Venue
International Conference on Computer Science and Information Technologies
Last Checked
4 months ago
Abstract
This paper contains analysis and extension of exploiters-based knowledge extraction methods, which allow generation of new knowledge, based on the basic ones. The main achievement of the paper is useful features of some universal exploiters proof, which allow extending set of basic classes and set of basic relations by finite set of new classes of objects and relations among them, which allow creating of complete lattice. Proposed approach gives an opportunity to compute quantity of new classes, which can be generated using it, and quantity of different types, which each of obtained classes describes; constructing of defined hierarchy of classes with determined subsumption relation; avoidance of some problems of inheritance and more efficient restoring of basic knowledge within the database.
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