Mutual Transformation of Information and Knowledge
Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL ontologies from the Web, generating rules from the found ontologies and supplementing the SWES knowledge base with these rules. The purpose of this paper is to show knowledge transformation into information by the example of ontology generation from rules.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Inverse Learning of Symmetries
Symmetry transformations induce invariances which are frequently described with deep latent variable models. In many complex domains, such as the chemical space, invariances can be observed, yet the corresponding symmetr…
Information based Deep Clustering: An experimental study
Recently, two methods have shown outstanding performance for clustering images and jointly learning the feature representation. The first, called Information Maximiz-ing Self-Augmented Training (IMSAT), maximizes the mut…
ClusteringDeep ClusteringImproving Numerical Stability of Normalized Mutual Information Estimator on High Dimensions
Mutual information provides a powerful, general-purpose metric for quantifying the amount of shared information between variables. Estimating normalized mutual information using a k-Nearest Neighbor (k-NN) based approach…
Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations
Transformation Equivariant Representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of {\em translation} equivariance underlying the success o…
TranslationTopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
We present the Topology Transformation Equivariant Representation (TopoTER) learning, a general paradigm of unsupervised learning of node representations of graph data for the wide applicability to Graph Convolutional Ne…
Graph Classification