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Collective knowledge graph: meta knowledge transfer and federated graph reasoning
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 1, Pages: 55-64(2022)
- 作者机构：
1. 浙江大学计算机科学与技术学院，浙江 杭州 310007
2. 浙江大学软件学院，浙江 杭州 310007
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202217
CLC： TP391-
Published Online：2022-03，
Published：15 March 2022
- 稿件说明：
移动端阅览
Mingyang CHEN, Wen ZHANG, Xiangnan CHEN, et al. Collective knowledge graph: meta knowledge transfer and federated graph reasoning[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 55-64.
Mingyang CHEN, Wen ZHANG, Xiangnan CHEN, et al. Collective knowledge graph: meta knowledge transfer and federated graph reasoning[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 55-64. DOI： 10.11959/j.issn.2096-6652.202217.
摘要
群体知识图谱是指通过群体协作，以去中心化或分布式方式管理和维护的知识图谱。相比现有的集中式管理的知识图谱，群体知识图谱具备知识确权、隐私保护、众包激励、可信溯责等特点。尝试探讨构建或应用群体知识图谱平台面临的技术挑战。其中分布式知识迁移考虑在一个分散自治的框架下，通过实现不同来源的多个知识图谱之间的知识迁移，缓解单个知识图谱的知识不完备问题。其主要难点是在充分保护知识的自治所有权的前提下，尽可能共享有用的知识，以增强各自的知识图谱表示。联邦式图谱推理也是考虑在一个分布式环境下，通过联邦学习机制实现隐私保护前提下的知识图谱推理。在分布式知识迁移中，强调在关系集合互相重叠的知识图谱间迁移与实体无关的知识；而在联邦式图谱推理中，强调在多个实体集合互相重叠的知识图谱间共同学习更好的实体嵌入表示。针对这两个问题分别进行模型设计及实验验证。
Abstract
Collective knowledge graphs refer to knowledge graphs that are managed and maintained in a decentralized or distributed manner through group collaboration.Compared with the existing centrally managed knowledge graph
the collective knowledge graph has the characteristics of knowledge right confirmation
privacy protection
crowd sourcing incentive
and credible traceability.Tring to explore the technical challenges faced by building and applying a collective knowledge graph platform.For meta knowledge transfer
the knowledge incompleteness of a single knowledge graph by knowledge transfer among multiple knowledge graphs from different sources under a decentralized and autonomous framework was considered.The main difficulty was to enhance the respective knowledge graph representation by sharing useful knowledge with each other as much as possible while fully protecting the autonomous ownership of knowledge.For federated graph reasoning
the knowledge graph reasoning in a distributed environment under the privacy-preserving by means of the federated learning mechanism was considered.Meta knowledge transfer focused on transferring entity-independent knowledge between knowledge graphs with overlapped relation set
while federated graph reasoning aimed at learning better entity embeddings for knowledge graphs with overlapped entity set.The model design and experimental validation for each of these two problems were conducted.
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references
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