{"url":"/sota/complex-query-answering-on-nell-995","task":{"name":"Complex Query Answering","url":"/task/complex-query-answering","note":null},"dataset":{"name":"NELL-995","url":"/dataset/nell-995"},"category":"Graphs","categories":["Graphs","Knowledge Base"],"category_note":null,"description":"This task is concerned with answering complex queries over incomplete knowledge graphs. In the most simple case, the task is reduced to link prediction: a 1-hop query for predicting the existence of an edge between a pair of nodes. Complex queries are concerned with other structures between nodes, such as 2-hop and 3-paths, and intersecting paths with intermediate variables.","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":["MRR 1p","MRR 2i","MRR 2p","MRR 2u","MRR 3i","MRR 3p","MRR ip","MRR pi","MRR up"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"MRR 1p":"higher","MRR 2i":"higher","MRR 2p":"higher","MRR 2u":"higher","MRR 3i":"higher","MRR 3p":"higher","MRR ip":"higher","MRR pi":"higher","MRR up":"higher"}},"counts":{"rows":6,"rows_with_code":5,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"QTO","metrics":{"MRR 1p":"0.607","MRR 2i":"0.425","MRR 2p":"0.241","MRR 2u":"0.204","MRR 3i":"0.506","MRR 3p":"0.216","MRR ip":"0.265","MRR pi":"0.313","MRR up":"0.179"},"uses_additional_data":false,"paper_date":"2022-12-19","paper":"/paper/answering-complex-logical-queries-on","paper_url":"https://arxiv.org/abs/2212.09567v3","paper_title":"Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization","code":"https://github.com/bys0318/qto","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":6,"n_samples":7,"n_pointer_only_licence":7}},{"rank_in_archive_order":2,"model":"CQD","metrics":{"MRR 1p":"0.604","MRR 2i":"0.436","MRR ip":"0.256"},"uses_additional_data":false,"paper_date":"2020-11-06","paper":"/paper/complex-query-answering-with-neural-link-1","paper_url":"https://arxiv.org/abs/2011.03459v4","paper_title":"Complex Query Answering with Neural Link Predictors","code":"https://github.com/uclnlp/cqd","n_code_links":5,"syntology":{"n_ran":5,"n_unverified":2,"n_samples":7,"n_pointer_only_licence":4}},{"rank_in_archive_order":3,"model":"CQDA","metrics":{"MRR 1p":"0.604","MRR 2i":"0.434","MRR 2p":"0.229","MRR 2u":"0.200","MRR 3i":"0.526","MRR 3p":"0.167","MRR ip":"0.264","MRR pi":"0.321","MRR up":"0.170"},"uses_additional_data":false,"paper_date":"2023-01-29","paper":"/paper/adapting-neural-link-predictors-for-complex","paper_url":"https://arxiv.org/abs/2301.12313v3","paper_title":"Adapting Neural Link Predictors for Data-Efficient Complex Query Answering","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"GNN-QE","metrics":{"MRR 1p":"0.533","MRR 2i":"0.424","MRR 2p":"0.189","MRR 2u":"0.159","MRR 3i":"0.525","MRR 3p":"0.149","MRR ip":"0.189","MRR pi":"0.308","MRR up":"0.126"},"uses_additional_data":false,"paper_date":"2022-05-16","paper":"/paper/neural-symbolic-models-for-logical-queries-on","paper_url":"https://arxiv.org/abs/2205.10128v2","paper_title":"Neural-Symbolic Models for Logical Queries on Knowledge Graphs","code":"https://github.com/DeepGraphLearning/GNN-QE","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"BetaE","metrics":{"MRR 1p":"0.53","MRR 2i":"0.376","MRR 2p":"0.13","MRR 2u":"0.122","MRR 3i":"0.475","MRR 3p":"0.114","MRR ip":"0.143","MRR pi":"0.241","MRR up":"0.085"},"uses_additional_data":false,"paper_date":"2020-10-22","paper":"/paper/beta-embeddings-for-multi-hop-logical","paper_url":"https://arxiv.org/abs/2010.11465v1","paper_title":"Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs","code":"https://github.com/snap-stanford/KGReasoning","n_code_links":9,"syntology":{"n_ran":8,"n_unverified":6,"n_samples":14,"n_pointer_only_licence":1}},{"rank_in_archive_order":6,"model":"Q2B","metrics":{"MRR 1p":"0.422","MRR 2i":"0.333","MRR 2p":"0.140","MRR 2u":"0.113","MRR 3i":"0.445","MRR 3p":"0.112","MRR ip":"0.168","MRR pi":"0.224","MRR up":"0.1103"},"uses_additional_data":false,"paper_date":"2020-02-14","paper":"/paper/query2box-reasoning-over-knowledge-graphs-in-1","paper_url":"https://arxiv.org/abs/2002.05969v2","paper_title":"Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings","code":"https://github.com/snap-stanford/KGReasoning","n_code_links":9,"syntology":{"n_ran":30,"n_unverified":11,"n_samples":41,"n_pointer_only_licence":2}}],"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":5,"rows_with_any_sample_ran":4,"distinct_papers_with_graph_line":5,"distinct_papers_with_any_sample_ran":4,"samples_over_distinct_papers":{"n_ran":44,"n_unverified":29,"n_samples":73,"n_pointer_only_licence":14,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":44,"n_unverified":29,"n_samples":73,"n_pointer_only_licence":14,"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"}}}