Papers › Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

21 Nov 2019arXiv:1911.09419archive 2025-07-28

Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang

Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model -- namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE) -- which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

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MIRALab-USTC/KGE-HAKE officialmentioned on GitHubpytorch report
MIRALab-USTC/KGE-DURA mentioned on GitHubpytorch report
guixiangyu1/KGE-HAKE-master mentioned on GitHubpytorchMIT report
kamigaito/icml2022 mentioned on GitHubpytorch report
miralab-ustc/qe-cone mentioned on GitHubpytorch report
thu-keg/eakit mentioned on GitHubpytorchMIT report
zyjcs/KGE-HAE mentioned on GitHubpytorchMIT report

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2ran · our draft was wrong
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parse_args MIRALab-USTC/KGE-HAKE/codes/runs.py official repository ran · our draft was wrong MIT (permissive) · 1a5f911b622c853e · report
parse_args kamigaito/icml2022/KGE-HAKE/codes/runs.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 1df6095f98d9a036 · report
boot_update_triple thu-keg/eakit/semi_utils.py community (archive-listed) unverified MIT (permissive) · b091391c5c3608e4 · report
bootstrapping thu-keg/eakit/semi_utils.py community (archive-listed) unverified MIT (permissive) · f888582ea241e0cb · report
div_list thu-keg/eakit/utils.py community (archive-listed) unverified MIT (permissive) · 3d325b122e3c6108 · report
filter_mat thu-keg/eakit/semi_utils.py community (archive-listed) unverified MIT (permissive) · 3d11d81dffd5eb22 · report
multi_cal_neg thu-keg/eakit/utils.py community (archive-listed) unverified MIT (permissive) · 7f9a46a57068bd32 · report
multi_cal_rank thu-keg/eakit/utils.py community (archive-listed) unverified MIT (permissive) · a6647903db5e8665 · report

Tasks

Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Knowledge Graph Completion FB15k-237 HAKE Hits@10 54.2 #2 of 4 Archive leaderboard report
Knowledge Graph Completion WN18RR HAKE Hits@3 0.516 #1 of 2 Archive leaderboard report
Link Prediction FB15k-237 HAKE Hits@1 0.25 #36 of 75 Archive leaderboard report
Link Prediction FB15k-237 HAKE Hits@3 0.381 #36 of 75 Archive leaderboard report
Link Prediction FB15k-237 HAKE MRR 0.346 #36 of 75 Archive leaderboard report
Link Prediction WN18RR HAKE Hits@1 0.452 #23 of 75 Archive leaderboard report
Link Prediction WN18RR HAKE Hits@10 0.582 #23 of 75 Archive leaderboard report
Link Prediction WN18RR HAKE Hits@3 0.516 #23 of 75 Archive leaderboard report
Link Prediction WN18RR HAKE MRR 0.497 #23 of 75 Archive leaderboard report
Link Prediction YAGO3-10 HAKE Hits@1 0.462 #11 of 18 Archive leaderboard report
Link Prediction YAGO3-10 HAKE Hits@10 0.694 #11 of 18 Archive leaderboard report
Link Prediction YAGO3-10 HAKE Hits@3 0.596 #11 of 18 Archive leaderboard report
Link Prediction YAGO3-10 HAKE MRR 0.545 #11 of 18 Archive leaderboard report

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