{"id":13605480,"url":"https://github.com/HKUST-KnowComp/WFRE","last_synced_at":"2025-04-12T05:33:15.510Z","repository":{"id":161509480,"uuid":"635405019","full_name":"HKUST-KnowComp/WFRE","owner":"HKUST-KnowComp","description":"Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport (Findings-ACL 2023)","archived":false,"fork":false,"pushed_at":"2023-05-04T10:18:17.000Z","size":84,"stargazers_count":11,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-08-02T19:37:42.097Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/HKUST-KnowComp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-05-02T16:12:46.000Z","updated_at":"2024-02-29T04:37:30.000Z","dependencies_parsed_at":null,"dependency_job_id":"385cde42-7c9d-415c-8f3c-8d76c79ffbfc","html_url":"https://github.com/HKUST-KnowComp/WFRE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HKUST-KnowComp%2FWFRE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HKUST-KnowComp%2FWFRE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HKUST-KnowComp%2FWFRE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HKUST-KnowComp%2FWFRE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HKUST-KnowComp","download_url":"https://codeload.github.com/HKUST-KnowComp/WFRE/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223497874,"owners_count":17155215,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T19:00:59.197Z","updated_at":"2024-11-07T10:30:40.587Z","avatar_url":"https://github.com/HKUST-KnowComp.png","language":"Python","funding_links":[],"categories":[":wrench: Implementations"],"sub_categories":["Dataset tools"],"readme":"# Wasseretein-Fisher-Rao Embedding\n\nThe implementation for ACL 2023 findings paper:\n\n\u003e Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport\n\nby Zihao Wang, Weizhi Fei, Hang Yin, Yangqiu Song, Ginny Y. Wong, and Simon See.\n\n# Prepare the data\n\nThe KG data (FB15k, FB15k-237, NELL995) should be put into under 'data/' folder. We use the [data](http://snap.stanford.edu/betae/KG_data.zip) provided in the [KGReasoning](https://github.com/snap-stanford/KGReasoning).\nThe structure of the data folder should be at least and we follow the query type of EFO-1 and transfer the data.\n```\ndata\n\t|---FB15k-237-betae\n\t|---FB15k-betae\n\t|---NELL-betae\n```\n\nThe OpsTree is generated by `binary_formula_iterator` in `fol/foq_v2.py`. The overall process is managed in `formula_generation.py`. Transform beta queries to EFO-1 is the next step.\n\nTo generate the formula and transform the queries data, just run\n```bash\npython formula_generation.py\npython transform_beta_data.py\n```\n\n\n# Run experiments on different knowledge graphs\nThe hyperparameters in three datasets is provided in config/papers.\nTo get our results in paper just run\n```bash\npython main.py -config config/papers/NELL.yaml\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHKUST-KnowComp%2FWFRE","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FHKUST-KnowComp%2FWFRE","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHKUST-KnowComp%2FWFRE/lists"}