ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training
This provides a standardized framework for researchers and practitioners to manage and query deep learning experiments, though it is incremental as it builds on existing ontology concepts for a specific domain.
The paper tackles the problem of describing complex deep learning configurations by introducing ANNETT-O, an ontology for describing artificial neural network evaluation, topology, and training, which enables researchers and practitioners to better design, train, and understand models through a generic and computer-actionable vocabulary.
Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly difficult for researchers and practitioners to design, train and understand them. In this paper we present ANNETT-O, a much-needed, generic and computer-actionable vocabulary for researchers and practitioners to describe their deep learning configurations, training procedures and experiments. The proposed ontology focuses on topological, training and evaluation aspects of complex deep neural configurations, while keeping peripheral entities more succinct. Knowledge bases implementing ANNETT-O can support a wide variety of queries, providing relevant insights to users. In addition to a detailed description of the ontology, we demonstrate its suitability to the task via a number of hypothetical use-cases of increasing complexity.