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|Author=Michael Färber
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|Author=York Sure-Vetter
 
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{{Publikation Details
 
{{Publikation Details
|Abstract=Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. For each of the neural networks in the FAIRnets Dataset, the relevant properties according to the Neural Network Ontology such as the description and the architecture are stored. Ultimately, the FAIRnets Dataset can be queried with a set of desired properties and responds with a set of neural networks that have these properties. We provide the service FAIRnets Search which is implemented on top of a SPARQL endpoint and allows for querying, searching and finding trained neural networks annotated with the Neural Network Ontology. The service is demonstrated by a browser-based frontend to the SPARQL endpoint.
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|Abstract=Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable (see the FAIR principles for scientific data management and stewardship). Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks based on this ontology, which we provide to the public on GitHub as a knowledge graph called FAIRnets for research purposes. Among other use cases, FAIRnets can be used by researchers and practitioners (e.g., data scientists) for finding suitable pre-trained neural networks given specific requirements by the users.
 
|Link=https://arxiv.org/abs/1907.11569
 
|Link=https://arxiv.org/abs/1907.11569
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 
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Aktuelle Version vom 30. Juli 2020, 16:09 Uhr


Making Neural Networks FAIR




Veröffentlichung: 2019 Juli
Art der Veröffentlichung: https://arxiv.org/abs/1907.11569
BibTeX

Kurzfassung
Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable (see the FAIR principles for scientific data management and stewardship). Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks based on this ontology, which we provide to the public on GitHub as a knowledge graph called FAIRnets for research purposes. Among other use cases, FAIRnets can be used by researchers and practitioners (e.g., data scientists) for finding suitable pre-trained neural networks given specific requirements by the users.

Weitere Informationen unter: Link



Forschungsgruppe

Web Science


Forschungsgebiet