群体知识图谱：分布式知识迁移与联邦式图谱推理

doi:10.11959/j.issn.2096-6652.202217

Abstract

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.

Key words: collective knowledge graph, federated learning, knowledge transfer

CLC Number:

TP391

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.

Figures/Tables 5

知识图谱表示学习方法	打分函数	向量空间
TransE	$- ∥ h + r - t ∥$	$h, r, t \in ℝ^{d}$
DistMult	$h^{⊤} diag (r) t$	$h, r, t \in ℝ^{d}$
ComplEx	$Re (h^{⊤} diag (r) \bar{t})$	$h, r, t \in ℂ^{d}$
RotatE	$- ∥ h \circ r - t ∥$	$h, r, t \in ℂ^{d}$

模型名称	WN18RR				FB15k-237				NELL-995
模型名称	v1	v2	v3	v4	v1	v2	v3	v4	v1	v2	v3	v4
Neural LP	74.4	69.0	46.2	67.1	52.9	58.9	52.9	55.9	40.8	78.7	82.7	$\underline{80.6}$
DRUM	74.4	69.0	46.2	67.1	52.9	58.7	52.9	55.9	19.4	78.6	82.7	$\underline{80.6}$
RuleN	80.9	78.2	53.4	71.6	49.8	77.8	$\underline{87.7}$	85.6	53.5	81.8	77.3	61.4
GraIL	82.5	78.7	58.4	73.4	64.1	81.8	82.8	$\underline{89.3}$	$\underline{59.5}$	93.3	91.4	73.1
CoMPILE	$\underline{83.6}$	$\underline{79.8}$	$\underline{60.7}$	$\underline{75.5}$	$\underline{67.6}$	$\underline{83.0}$	84.7	87.4	58.4	$\underline{93 . 9}$	$\underline{92 . 8}$	75.1
MorsE (TransE)	81.5	78.8	67.3	76.5	$84 . 7$	$96 . 3$	$95 . 8$	$96 . 4$	51.8	83.8	89.3	$82 . 4$
MorsE (DistMult)	83.7	80.0	66.1	79.3	82.5	94.9	94.9	95.8	$66 . 6$	79.4	84.3	54.1
MorsE (ComplEx)	83.5	81.4	67.3	79.4	80.0	92.8	93.6	95.1	65.0	76.9	82.3	42.6
MorsE (RotatE)	$84 . 1$	$81 . 5$	$70 . 9$	$79 . 6$	83.2	95.7	95.7	95.9	65.2	80.7	87.7	53.4

知识图谱表示学习方法	设置	客户端1		客户端2		客户端3
知识图谱表示学习方法	设置	MRR	Hits@10	MRR	Hits@10	MRR	Hits@10
	isolation	50.4	69.8	38.7	63.7	33.7	59.7
TransE	collection	54.2	74.1	41.0	66.9	35.7	63.1
	FedE	$\underline{54 . 8}$	$\underline{74 . 8}$	$\underline{41 . 8}$	$\underline{68 . 4}$	$\underline{36 . 3}$	$\underline{63 . 4}$
	isolation	48.9	65.2	36.6	56.2	34.7	57.0
DistMult	collection	51.7	70.1	38.2	59.9	33.9	56.4
	FedE	$\underline{53 . 6}$	$\underline{70 . 3}$	$\underline{40 . 4}$	$\underline{62 . 4}$	$\underline{37 . 5}$	$\underline{61 . 5}$
	isolation	48.4	65.3	37.2	57.8	32.6	55.7
ComplEx	collection	50.0	67.5	36.9	57.4	33.4	55.5
	FedE	$\underline{52 . 9}$	$\underline{70 . 2}$	$\underline{40 . 9}$	$\underline{63 . 2}$	$\underline{36 . 1}$	$\underline{59 . 5}$
	isolation	52.7	70.6	40.6	64.2	36.6	60.2
RotatE	collection	$55 . 7$	75.1	42.1	67.3	37.1	63.5
	FedE	$\underline{55.5}$	$\underline{75 . 4}$	$\underline{43 . 0}$	$\underline{68 . 5}$	$\underline{38 . 9}$	$\underset{⇁}{65 . 0}$

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