{"url":"/dataset/olpbench","name":"OLPBENCH","full_name":"OLPBENCH","description_markdown":"OLPBENCH is a large Open Link Prediction benchmark, which was derived from the state-of-the-art Open Information Extraction corpus OPIEC (Gashteovski et al., 2019). OLPBENCH contains 30M open triples, 1M distinct open relations and 2.5M distinct mentions of approximately 800K entities. \r\n\r\nOpen Link Prediction is defined as follows: Given an Open Knowledge Graph and a question consisting of an entity mention and an open relation, predict mentions as answers. A predicted mention is correct if it is a mention of the correct answer entity. For example, given the question (“NBC-TV”, “has office in”, ?), correct answers include “NYC” and “New York”.\r\n\r\nSource: [OLPBENCH](https://www.uni-mannheim.de/dws/research/resources/olpbench/)","description_withheld":null,"homepage":"https://www.uni-mannheim.de/dws/research/resources/olpbench/","introduced_date":"2020-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/can-we-predict-new-facts-with-open-knowledge","title":"Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction","first_author":"Samuel Broscheit","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Graph Embedding","url":"/task/graph-embedding","datasets_with_task":"/datasets/task/graph-embedding"},{"name":"Knowledge Graph Embeddings","url":"/task/knowledge-graph-embeddings","datasets_with_task":"/datasets/task/knowledge-graph-embeddings"},{"name":"Open Knowledge Graph Embedding","url":"/task/open-knowledge-graph-embedding","datasets_with_task":"/datasets/task/open-knowledge-graph-embedding"}],"languages":[],"variants":["OLPBENCH"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}