TENET: Joint Entity and Relation Linking with Coherence Relaxation

Summary: TENET relaxes global coherence in joint entity-relation linking by formulating it as a minimum-cost rooted-tree cover on a knowledge-coherence graph, unsupervised. Efficient approximation with pruning yields SOTA performance on real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6171
Venue: SIGMOD
Year: 2021
Pagerank: 4.1945683e-05
Overall Rank: 11,475 | 20.18%
DOI: 10.1145/3448016.3457280

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,615	OpenMEL: Unsupervised Multimodal Entity Linking Using Noise-Free Expanded Queries and Global Coherence	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,878	Query-Driven On-The-Fly Knowledge Base Construction	2018	VLDB	0.00010233436
5,041	KBPearl: A Knowledge Base Population System Supported by Joint Entity and Relation Linking	2020	VLDB	5.741618e-05

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