{"id":13605257,"url":"https://github.com/lilv98/LQAC","last_synced_at":"2025-04-12T05:32:22.610Z","repository":{"id":111546202,"uuid":"596269242","full_name":"lilv98/LQAC","owner":"lilv98","description":"Beyond Knowledge Graphs: Neural Logical Reasoning with Ontologies","archived":false,"fork":false,"pushed_at":"2023-08-01T04:09:17.000Z","size":2212,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-07T10:40:56.736Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[":wrench: Implementations"],"sub_categories":["Dataset tools"],"readme":"# Neural Multi-hop Logical Query Answering with Concept-level Answers\n\n# Requirements\n* python == 3.8.5\n* torch == 1.8.1\n* numpy == 1.19.2\n* pandas == 1.0.1\n* tqdm == 4.61.0\n* groovy == 4.0.0\n* JVM == 1.8.0_333\n\n# Datasets\n\n## YAGO4\n### Using the pre-processed datasets\nDownload and unzip YAGO4.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace\n\n    ./data/YAGO4/input/\n\n### Dataset Construction\nDownload the following files: [*T*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-class.nt.gz), \n[*A\u003csub\u003eee*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-facts.nt.gz),\n[*A\u003csub\u003eec1*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-full-types.nt.gz),\nand [*A\u003csub\u003eec2*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-simple-types.nt.gz)\n\nUnzip the files to:\n\n    ./data/YAGO4/raw/\n\nRun all cells in:\n\n    ./code/ppc_YAGO4/raw2mid.ipynb\n    ./code/ppc_YAGO4/ppc.ipynb\n\n\n## DBpedia\n### Using pre-processed datasets\nDownload and unzip DBpedia.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace\n\n    ./data/DBpedia/input/\n\n### Dataset Construction\nDownload the following files: [*T*](http://downloads.dbpedia.org/2016-10/dbpedia_2016-10.nt),\n[*A\u003csub\u003eee*](http://downloads.dbpedia.org/2016-10/core-i18n/en/mappingbased_objects_wkd_uris_en.ttl.bz2), and \n[*A\u003csub\u003eec*](http://downloads.dbpedia.org/2016-10/core-i18n/en/instance_types_transitive_wkd_uris_en.ttl.bz2)\n\nUnzip the files to:\n\n    ./data/DBpedia/raw/\n\nRun all cells in:\n\n    ./code/ppc_DBpedia/raw2mid.ipynb\n    ./code/ppc_DBpedia/ppc.ipynb\n\n\n## Gene Ontology (GO)\n### Using pre-processed datasets\nDownload and unzip GO.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace\n\n    ./data/GO/input/\n\n### Dataset Construction\n\nDownload the raw data [here](https://bio2vec.cbrc.kaust.edu.sa/data/elembeddings/el-embeddings-data.zip) and unzip it to:\n\n    ./data/GO/raw/\n\nGenerate axioms using:\n\n    groovy ./code/ppc_GO/GetOntology.groovy ./data/GO/raw/data-train/yeast-classes.owl \u003e ./data/GO/raw/ontology.txt\n\nGenerate intermediate data using:\n\n    cd ./code/ppc_GO/ \u0026\u0026 python raw2mid.py\n\nRun all cells in:\n\n    ./code/ppc_GO/ppc.ipynb\n\n\n\n# Run\nTo reproduce the main results, simply run the following commands:\n\n    python TAR.py --dataset YAGO4\n    python TAR.py --dataset DBpedia\n    python TAR.py --dataset GO\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flilv98%2FLQAC","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flilv98%2FLQAC","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flilv98%2FLQAC/lists"}