{"url":"/dataset/yago","name":"YAGO","full_name":"Yet Another Great Ontology","description_markdown":"**Yet Another Great Ontology** (**YAGO**) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge base. YAGO originally consisted of more than 1 million entities and 5 million facts describing relationships between these entities. YAGO2 grounded entities, facts, and events in time and space, contained 446 million facts about 9.8 million entities, while YAGO3 added about 1 million more entities from non-English Wikipedia articles. YAGO3-10 a subset of YAGO3, containing entities which have a minimum of 10 relations each.\r\n\r\nSource: [Recent Advances in Natural Language Inference:A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172)\r\nImage Source: [https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/)","description_withheld":null,"homepage":"https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/","introduced_date":"2007-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Yago: a core of semantic knowledge","first_author":null,"url":"https://doi.org/10.1145/1242572.1242667"},"license":{"name":"CC BY 3.0","url":"https://creativecommons.org/licenses/by/3.0/"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Time-interval Prediction","url":"/task/time-interval-prediction","datasets_with_task":"/datasets/task/time-interval-prediction"},{"name":"Triple Classification","url":"/task/triple-classification","datasets_with_task":"/datasets/task/triple-classification"}],"languages":[],"variants":["YAGO3-10","YAGO15k","YAGO39K","YAGO37","Yago11k","YAGO"],"data_loaders":[],"num_papers_in_archive":359,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/link-prediction-on-yago15k-1","task":"Link Prediction","dataset_variant":"YAGO15k","rows":2,"metrics":["MRR"],"first_row_in_archive_order":{"model":"TNTComplEx (x10)","paper":"/paper/tensor-decompositions-for-temporal-knowledge-1","metrics":{"MRR":"0.37"},"code_links":[{"title":"apoorvumang/CronKGQA","url":"https://github.com/apoorvumang/CronKGQA"},{"title":"facebookresearch/tkbc","url":"https://github.com/facebookresearch/tkbc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-yago37","task":"Link Prediction","dataset_variant":"YAGO37","rows":2,"metrics":["Hits@1","Hits@10","Hits@3","Hits@5","MRR"],"first_row_in_archive_order":{"model":"SEEK","paper":"/paper/seek-segmented-embedding-of-knowledge-graphs","metrics":{"Hits@1":"0.370","Hits@10":"0.622","Hits@3":"0.498","MRR":"0.454"},"code_links":[{"title":"Wentao-Xu/SEEK","url":"https://github.com/Wentao-Xu/SEEK"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-yago11k","task":"Link Prediction","dataset_variant":"Yago11k","rows":1,"metrics":["MRR"],"first_row_in_archive_order":{"model":"TimePlex","paper":"/paper/temporal-knowledge-base-completion-new","metrics":{"MRR":"0.2364"},"code_links":[{"title":"dair-iitd/tkbi","url":"https://github.com/dair-iitd/tkbi"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-yago39k","task":"Link Prediction","dataset_variant":"YAGO39K","rows":1,"metrics":["Hits@1","Hits@10","Hits@3","MRR"],"first_row_in_archive_order":{"model":"TransC (bern)","paper":"/paper/differentiating-concepts-and-instances-for","metrics":{"Hits@1":"0.298","Hits@10":"0.698","Hits@3":"0.502","MRR":"0.42"},"code_links":[{"title":"davidlvxin/TransC","url":"https://github.com/davidlvxin/TransC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/triple-classification-on-yago39k","task":"Triple Classification","dataset_variant":"YAGO39K","rows":1,"metrics":["Accuracy","F1-Score","Precision","Recall"],"first_row_in_archive_order":{"model":"TransC (bern)","paper":"/paper/differentiating-concepts-and-instances-for","metrics":{"Accuracy":"93.8","F1-Score":"93.7","Precision":"94.8","Recall":"92.7"},"code_links":[{"title":"davidlvxin/TransC","url":"https://github.com/davidlvxin/TransC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/temporal-knowledge-base-completion-new","title":"Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols","date":"2020-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/seek-segmented-embedding-of-knowledge-graphs","title":"SEEK: Segmented Embedding of Knowledge Graphs","date":"2020-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tensor-decompositions-for-temporal-knowledge-1","title":"Tensor Decompositions for temporal knowledge base completion","date":"2020-04-10","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/differentiating-concepts-and-instances-for","title":"Differentiating Concepts and Instances for Knowledge Graph Embedding","date":"2018-11-12","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/knowledge-graph-embedding-with-iterative","title":"Knowledge Graph Embedding with Iterative Guidance from Soft Rules","date":"2017-11-30","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":3,"samples_harvested":16,"samples_ran":7,"samples_unverified":9,"pointer_only_for_licence":16,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}