Learning to map ontologies with neural network

The second step represents the knowledge extraction process which aims to identify the useful knowledge from the input biological documents and extract the candidate instances

According to (1) output of neural network is just a sum of random weights multiplied by initial values, the only param- eter that might be modified in neural network are weights

Harmonization between the species-specific ontologies and the Planteome reference ontologies is currently done by mapping Crop Ontology to the Plant Trait Ontology (TO)

To cope with the reasoning, the model was separated in three parts: the knowledge schema of the domain (the partonomy that aggregates each aspect of a rock that needs to be

The correlation method is used to reduce the feature dimension in order to find suitable features for both arousal and valence.. In future, we want to explore

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

We do not discuss the problem of storage capability but compare our results on retrieval of spurious state with the ones derived in the Hopfield.. model

Abstract. 2014 I extend the class of exactly solvable feed-forward neural networks discussed in a previous publication. Three basic modifications of the original