Complex Query Answering On Nell 995

Métriques

MRR 1p

MRR 2i

MRR 2p

MRR 2u

MRR 3i

MRR 3p

MRR ip

MRR pi

MRR up

Résultats

Résultats de performance de divers modèles sur ce benchmark

										Paper Title
QTO	0.607	0.425	0.241	0.204	0.506	0.216	0.265	0.313	0.179	Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
CQDA	0.604	0.434	0.229	0.200	0.526	0.167	0.264	0.321	0.170	Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
CQD	0.604	0.436	-	-	-	-	0.256	-	-	Complex Query Answering with Neural Link Predictors
GNN-QE	0.533	0.424	0.189	0.159	0.525	0.149	0.189	0.308	0.126	Neural-Symbolic Models for Logical Queries on Knowledge Graphs
BetaE	0.53	0.376	0.13	0.122	0.475	0.114	0.143	0.241	0.085	Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Q2B	0.422	0.333	0.140	0.113	0.445	0.112	0.168	0.224	0.1103	Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

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Complex Query Answering On Nell 995 | SOTA | HyperAI