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We present the FAIRnets Knowledge Graph, a large RDF data set with information about neural networks. The data set is based on neural networks published on GitHub using Keras as framework. Furthermore, the data set is implemented in FAIRnets Search (https://km.aifb.kit.edu/services/fairnets/), an online search system by which users can explore and analyze neural networks.
\n\nMore information can be found at https://km.aifb.kit.edu/services/fairnets/ and in the paper Making Neural Networks FAIR (https://arxiv.org/abs/1907.11569).
\n\nIf you use the data set, please cite it as follows (see also in DBLP):
\n\n\n", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2019-04-09", "publisher": "Zenodo", "references": [ { "reference": "Ngyuen, Anna, et al. (2020). Making Neural Networks FAIR. Knowledge Graphs and Semantic Web Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020, M\u00e9rida, Mexico, November 26-27, 2020, Proceedings" } ], "related_identifiers": [ { "identifier": "http://w3id.org/nno/data", "relation_type": { "id": "isidenticalto", "title": { "de": "Ist identisch mit", "en": "Is identical to" } }, "scheme": "url" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Neural Network, FAIR, Ontology, Reusability" } ], "title": "FAIRnets Dataset", "version": "3.1" }, "parent": { "access": { "owned_by": { "user": 64634 } }, "communities": { "default": "b638e3e5-64a3-451b-a2cd-1e27ffa319a3", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2014-06-06T03:54:06+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "b638e3e5-64a3-451b-a2cd-1e27ffa319a3", "links": {}, "metadata": { "curation_policy": "Nguyen, Anna, Tobias Weller, Michael Färber and York Sure-Vetter. “Making Neural Networks FAIR.” Knowledge Graphs and Semantic Web Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020, Mérida, Mexico, November 26-27, 2020, Proceedings
\n
Accepting published peer-reviewed publications; recommendations and standards; open source software; vocabularies and ontologies; linked data datasets; theses and student reports.
\r\n", "page": "Linked Data is a way to publish structured information on the Web, using well-defined properties with links related resources, all identified with accessible URIs.
\r\n\r\nLinked Data builds on (and includes) Semantic Web technologies like RDF, HTTP, REST and JSON-LD, and has a strong emphasis on openess and reuse of identifiers. Interoperability and ease of use has higher priority than reaching ideal semantic clarity.
\r\n\r\nLinked Data is also an important part of the Open Data movement, as it standardizes how to structure data and merging multiple disparate data sets is made possible using common identifiers and vocabularies.
\r\n\r\nThis community's logo is adapted from the Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
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