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|Rank=2
 
|Rank=2
 
|Author=Tobias Weller
 
|Author=Tobias Weller
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{{Publikation Author
 
|Rank=3
 
|Author=York Sure-Vetter
 
 
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}}
 
{{Inproceedings
 
{{Inproceedings
|Title=Making Neural Networks FAIR
+
|Referiert=Ja
 +
|Title=FAIRnets Search - A Prototype Search Service to Find Neural Networks
 
|Year=2019
 
|Year=2019
|Month=Juli
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|Booktitle=Poster&Demos at SEMANTICS 2019
|Howpublished=https://arxiv.org/abs/1907.11569
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|Organization=Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems
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|Publisher=CEUR Workshop Proceedings
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|Volume=2451
 
}}
 
}}
 
{{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 momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.
|Link=https://arxiv.org/abs/1907.11569
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|ISBN=1613-0073
 +
|Download=FAIRnets_Search.pdf
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 
}}
 
}}

Aktuelle Version vom 7. Oktober 2019, 10:44 Uhr


FAIRnets Search - A Prototype Search Service to Find Neural Networks


FAIRnets Search - A Prototype Search Service to Find Neural Networks



Published: 2019

Buchtitel: Poster&Demos at SEMANTICS 2019
Ausgabe: 2451
Verlag: CEUR Workshop Proceedings
Organisation: Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems

Referierte Veröffentlichung

BibTeX

Kurzfassung
Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.

ISBN: 1613-0073
Download: Media:FAIRnets_Search.pdf



Forschungsgruppe

Web Science


Forschungsgebiet