时态知识图谱补全的方法及其进展

doi:10.11959/j.issn.2096-0271.2021024

Abstract

Abstract:

Temporal knowledge graph (TKG) are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently, TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG completion were summarized, one based on symbolic logic whereas and the other based on knowledge representation learning.The pros and cons of these two different methodologies were discussed, highlighting some directions for enhancing TKG completion in future research.Also, seven benchmark datasets for TKG completion and evaluation results of several typical models on the benchmark datasets were introduced.

Key words: temporal knowledge graph, ontology, representation learning

CLC Number:

TP18

Yuming SHEN, Jianfeng DU. Temporal knowledge graph completion:methods and progress[J]. Big Data Research, 2021, 7(3): 30-41.

Figures/Tables 5

References 42

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数据集	实体数/个	关系数/条	时间跨度	更新时间间隔
GDELT-500	500	20	2015—2016年	15 min
ICEWS14	7 128/6 869	230	2014年	每天
ICEWS05-15	10 094	251	2005—2015年	每天
Wikidata11k	11 134	95	25—2020年	每年
Wikidata12k	12 554	24	1479—2018年	每年
YAGO11k	10 623	10	公元前453—2844年	每年
YAGO15k	15 403	34	1513—2017年	每年

模型		ICEWS14			ICEWS05-15			Wikidata11k
模型	MRR	Hit@1	Hit@10	MRR	Hit@1	Hit@10	MRR	Hit@1	Hit@10
TransE^*	0.280	9.4	63.7	0.294	9.0	66.3	0.316	18.1	65.9
DistMult^*	0.439	32.3	67.2	0.456	33.7	69.1	0.316	18.1	66.1
ConT⁺	0.185	11.7	31.5	0.163	10.5	27.2	-	-	-
TTransE^*	0.255	7.4	60.1	0.271	8.4	61.6	0.488	33.9	80.6
HyTE⁺	0.297	10.8	65.5	0.316	11.6	68.1	-	-	-
TA-TransE^*	0.275	9.5	62.5	0.299	9.6	66.8	0.484	32.9	80.7
TA-DistMult^*	0.477	36.3	68.6	0.474	34.6	72.8	0.700	65.2	78.5
DE-SimplE⁺	0.526	41.8	72.5	0.513	39.2	74.8	0.310	18.4	62.5
TComplEx^#	0.560	47.0	73.0	0.580	49.0	76.0	0.731	67.3	84.5
TNTComplEx^#	0.560	46.0	74.0	0.600	50.0	78.0	0.718	65.4	85.6

Temporal knowledge graph completion:methods and progress

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