Whyis 2: An Open Source Framework for Knowledge Graph Development and Research
Whyis 2: An Open Source Framework for Knowledge Graph Development and Research
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Whyis 2:知识图开发和研究的开源框架
DOI:
10.1007/978-3-031-33455-9_32
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发表时间:
2023
期刊:
影响因子:
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通讯作者:
McGuinness, Deborah L
中科院分区:
文献类型:
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作者:
McCusker, Jamie;McGuinness, Deborah L
Whyis is the first open source framework for creating custom provenance-driven knowledge graph applications, orKGApps, supporting three principal tasks: knowledge curation, inference, and interaction. It has been used in knowledge graph projects in materials science, health informatics, and radio spectrum policy. All knowledge in Whyis graphs are encapsulated in nanopublications, which simplifies and standardizes the production of qualified knowledge in knowledge graphs. The architecture of Whyis enables what we consider to be essential requirements for knowledge graph construction, maintenance, and use. These requirements include support for automated and manual curation of knowledge from diverse sources, provenance traces of all knowledge, domain-specific user interaction, and generalized distributed knowledge inference. We coin the term “Nano-scale knowledge graph” to refer to nanopublication-driven knowledge graphs. Knowledge graph developers can use Whyis to configure custom sets of knowledge curation pipelines using custom data importers and semantic extract, transform, and load scripts. The flexible, nanopublication-based architecture of Whyis lets knowledge graph developers integrate, extend, and publish knowledge from heterogeneous sources on the web. Whyis KGApps and are easily developed locally, managed using source control, and deployable via continuous integration, server deployment scripts, and as docker containers.
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DOI:
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发表时间:
1998
期刊:
影响因子:
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作者:
A. Simmonds
通讯作者:
A. Simmonds
影响因子:
2.5
作者:
Bizer, Christian;Lehmann, Jens;Hellmann, Sebastian
通讯作者:
Hellmann, Sebastian
影响因子:
3.8
作者:
M. Krötzsch;Denny Vrandečić
通讯作者:
Denny Vrandečić
DOI:
10.1145/3340531.3412768
发表时间:
2020
期刊:
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
影响因子:
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作者:
Lu Zhou;C. Shimizu;P. Hitzler;Alicia M. Sheill;Seila Gonzalez Estrecha;Catherine Foley;D. Tarr;Dean Rehberger
通讯作者:
Dean Rehberger
DOI:
10.4135/9781529774313
发表时间:
2021
期刊:
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影响因子:
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作者:
通讯作者:
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