OpenCodePapers

complex-query-answering-on-fb15k-237

Inductive knowledge graph completionLarge Language ModelKnowledge GraphsComplex Query Answering

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Paper	Code	MRR 1p	MRR 2i	MRR 2p	MRR 2u	MRR 3i	MRR 3p	MRR ip	MRR pi	MRR up	Hits@3 1p	Hits@3 2i	Hits@3 2p	Hits@3 2u	Hits@3 3i	Hits@3 3p	Hits@3 ip	Hits@3 pi	Hits@3 up	ModelName	ReleaseDate
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization	✓ Link	0.490	0.431	0.214	0.227	0.568	0.212	0.280	0.381	0.214										QTO	2022-12-19
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering		0.467	0.345	0.136	0.176	0.483	0.114	0.209	0.274	0.114										CQDA	2023-01-29
Neural-Symbolic Models for Logical Queries on Knowledge Graphs	✓ Link	0.428	0.383	0.147	0.162	0.541	0.118	0.189	0.311	0.134										GNN-QE	2022-05-16
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings	✓ Link	0.406	0.295	0.094	0.113	0.423	0.068	0.126	0.212	0.076										Q2B	2020-02-14
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs	✓ Link	0.39	0.288	0.109	0.124	0.425	0.1	0.126	0.224	0.097										BetaE	2020-10-22
Embedding Logical Queries on Knowledge Graphs	✓ Link	0.35	0.233	0.072	0.082	0.346	0.053	0.107	0.165	0.057										GQE	2018-06-05
Complex Query Answering with Neural Link Predictors	✓ Link					0.486														CQD	2020-11-06
Complex Query Answering with Neural Link Predictors	✓ Link										0.512	35.2	0.213	0.281	0.457	0.131	0.146	0.222	0.132	CQD-CO	2020-11-06
Complex Query Answering with Neural Link Predictors	✓ Link										0.512	0.352	0.288	0.284	0.457	0.221	0.129	0.249	0.121	CQD-Beam	2020-11-06