{"url":"/dataset/pathquestion","name":"PathQuestion","full_name":null,"description_markdown":"Adopts two subsets of Freebase (Bollacker et al., 2008) as Knowledge Bases to construct the PathQuestion (PQ) and the PathQuestion-Large (PQL) datasets. Paths are extracted between two entities which span two hops (es → r1 → e1 → r2 → a, denoted by -2H) or three hops (es→ r1 → e1 →r2 → e2→ r3 → a, denoted by -3H) and then generated natural language questions with templates. To make the generated questions analogical to real-world questions, paraphrasing templates and synonyms for relations are included by searching the Internet and two real-world datasets, WebQuestions (Berant et al., 2013) and WikiAnswers (Fader et al., 2013). In this way, the syntactic structure and surface wording of the generated questions have been greatly enriched.\r\n\r\nSource: [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://aclanthology.org/C18-1171.pdf)","description_withheld":null,"homepage":"https://github.com/zmtkeke/IRN","introduced_date":"2018-01-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-interpretable-reasoning-network-for-multi","title":"An Interpretable Reasoning Network for Multi-Relation Question Answering","first_author":"Mantong Zhou","url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"KG-to-Text Generation","url":"/task/kg-to-text","datasets_with_task":"/datasets/task/kg-to-text"}],"languages":[],"variants":["PathQuestion"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/kg-to-text-generation-on-pathquestion","task":"KG-to-Text Generation","dataset_variant":"PathQuestion","rows":5,"metrics":["BLEU","METEOR","ROUGE"],"first_row_in_archive_order":{"model":"JointGT (BART)","paper":"/paper/jointgt-graph-text-joint-representation","metrics":{"BLEU":"65.89","METEOR":"48.25","ROUGE":"78.87"},"code_links":[{"title":"thu-coai/JointGT","url":"https://github.com/thu-coai/JointGT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/jointgt-graph-text-joint-representation","title":"JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs","date":"2021-06-19","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/toward-subgraph-guided-knowledge-graph","title":"Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks","date":"2020-04-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"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":1,"samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"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."}