{"url":"/sota/knowledge-graph-completion-on-fb15k-237","task":{"name":"Knowledge Graph Completion","url":"/task/knowledge-graph-completion","note":null},"dataset":{"name":"FB15k-237","url":"/dataset/fb15k-237"},"category":"Natural Language Processing","categories":["Graphs","Knowledge Base","Natural Language Processing"],"category_note":null,"description":"Knowledge graphs $G$ are represented as a collection of triples $\\\\{(h, r, t)\\\\}\\subseteq E\\times R\\times E$, where $E$ and $R$ are the entity set and relation set. The task of **Knowledge Graph Completion** is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.\r\n\r\n\r\n<span class=\"description-source\">Source: [One-Shot Relational Learning for Knowledge Graphs ](https://arxiv.org/abs/1808.09040)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Hits@10","Hits@1","Hits@3","MR","MRR"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Hits@10":null,"Hits@1":null,"Hits@3":null,"MR":null,"MRR":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"KBGAT","metrics":{"Hits@10":"62.6","Hits@3":"54"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/learning-attention-based-embeddings-for","paper_url":"https://arxiv.org/abs/1906.01195v1","paper_title":"Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs","code":"https://github.com/deepakn97/relationPrediction","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":2,"model":"HAKE","metrics":{"Hits@10":"54.2"},"uses_additional_data":false,"paper_date":"2019-11-21","paper":"/paper/learning-hierarchy-aware-knowledge-graph","paper_url":"https://arxiv.org/abs/1911.09419v3","paper_title":"Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction","code":"https://github.com/MIRALab-USTC/KGE-HAKE","n_code_links":9,"syntology":{"n_ran":2,"n_unverified":6,"n_samples":8,"n_pointer_only_licence":1}},{"rank_in_archive_order":3,"model":"PKGC","metrics":{"Hits@10":"48.7"},"uses_additional_data":false,"paper_date":"2021-11-16","paper":"/paper/do-pre-trained-models-benefit-knowledge-graph","paper_url":"https://openreview.net/forum?id=_wHAe6eoT8","paper_title":"Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"KBAT","metrics":{"Hits@1":"46","MR":"0.210","MRR":"0.518"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/learning-attention-based-embeddings-for","paper_url":"https://arxiv.org/abs/1906.01195v1","paper_title":"Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs","code":"https://github.com/deepakn97/relationPrediction","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":3,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":6,"n_samples":9,"n_pointer_only_licence":2,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":4,"n_unverified":6,"n_samples":10,"n_pointer_only_licence":3,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}