{"url":"/dataset/nell","name":"NELL","full_name":"Never Ending Language Learning","description_markdown":"**NELL** is a dataset built from the Web via an intelligent agent called Never-Ending Language Learner. This agent attempts to learn over time to read the web. NELL has accumulated over 50 million candidate beliefs by reading the web, and it is considering these at different levels of confidence. NELL has high confidence in 2,810,379 of these beliefs.\r\n\r\nSource: [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://arxiv.org/abs/2002.00388)\r\nImage Source: [http://rtw.ml.cmu.edu/rtw/](http://rtw.ml.cmu.edu/rtw/)","description_withheld":null,"homepage":"http://rtw.ml.cmu.edu/rtw/","introduced_date":"2010-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Toward an Architecture for Never-Ending Language Learning","first_author":null,"url":"http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1879"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"},{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Complex Query Answering","url":"/task/complex-query-answering","datasets_with_task":"/datasets/task/complex-query-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NELL","NELL-995","NELL995"],"data_loaders":[{"repo":"https://github.com/rusty1s/pytorch_geometric","url":"https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html","frameworks":["pytorch"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/rtw-cmu/nell","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/asoria/nell","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/nell","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":177,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-nell","task":"Node Classification","dataset_variant":"NELL","rows":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DFNet-ATT","paper":"/paper/dfnets-spectral-cnns-for-graphs-with-feedback","metrics":{"Accuracy":"68.8 ± 0.3"},"code_links":[{"title":"wokas36/DFNets","url":"https://github.com/wokas36/DFNets"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/complex-query-answering-on-nell995","task":"Complex Query Answering","dataset_variant":"NELL995","rows":2,"metrics":["Hits@3 2p","Hits@3 3p","Hits@3 ip","Hits@3 pi","Hits@3 up","Hits@3 1p","Hits@3 2i","Hits@3 2u","Hits@3 3i"],"first_row_in_archive_order":{"model":"CQD-Beam","paper":"/paper/complex-query-answering-with-neural-link-1","metrics":{"Hits@3 2p":"0.350","Hits@3 3p":"0.288","Hits@3 ip":"0.171","Hits@3 pi":"0.277","Hits@3 up":"0.156"},"code_links":[{"title":"uclnlp/cqd","url":"https://github.com/uclnlp/cqd"},{"title":"dice-group/dice-embeddings","url":"https://github.com/dice-group/dice-embeddings"},{"title":"pminervini/kgreasoning","url":"https://github.com/pminervini/kgreasoning"},{"title":"LHY-24/KG-Compilation","url":"https://github.com/LHY-24/KG-Compilation"},{"title":"Blidge/KGReasoning","url":"https://github.com/Blidge/KGReasoning"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/complex-query-answering-with-neural-link-1","title":"Complex Query Answering with Neural Link Predictors","date":"2020-11-06","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dfnets-spectral-cnns-for-graphs-with-feedback","title":"DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters","date":"2019-10-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-adversarial-training-dynamically","title":"Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure","date":"2019-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":58,"samples_ran":31,"samples_unverified":27,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-semi-supervised-learning-with","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","date":"2016-03-29","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":15,"samples_unverified":13,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":95,"samples_ran":51,"samples_unverified":44,"pointer_only_for_licence":30,"papers_with_no_sample_that_ran":1,"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."}