https://github.com/lilv98/LQAC

Beyond Knowledge Graphs: Neural Logical Reasoning with Ontologies
https://github.com/lilv98/LQAC

Last synced: about 2 months ago
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Beyond Knowledge Graphs: Neural Logical Reasoning with Ontologies

• Host: GitHub
• URL: https://github.com/lilv98/LQAC
• Owner: lilv98
• License: bsd-2-clause
• Created: 2023-02-01T20:27:22.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-08-01T04:09:17.000Z (over 1 year ago)
• Last Synced: 2024-10-09T10:05:54.916Z (3 months ago)
• Language: Jupyter Notebook
• Size: 2.11 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-logical-query - TAR

README

        
# Neural Multi-hop Logical Query Answering with Concept-level Answers

# Requirements

* python == 3.8.5

* torch == 1.8.1

* numpy == 1.19.2

* pandas == 1.0.1

* tqdm == 4.61.0

* groovy == 4.0.0

* JVM == 1.8.0_333

# Datasets

## YAGO4

### Using the pre-processed datasets

Download and unzip YAGO4.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace

    ./data/YAGO4/input/

### Dataset Construction

Download the following files: [*T*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-class.nt.gz), 

[*Aee*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-facts.nt.gz),

[*Aec1*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-full-types.nt.gz),

and [*Aec2*](https://yago-knowledge.org/data/yago4/en/2020-02-24/yago-wd-simple-types.nt.gz)

Unzip the files to:

    ./data/YAGO4/raw/

Run all cells in:

    ./code/ppc_YAGO4/raw2mid.ipynb

    ./code/ppc_YAGO4/ppc.ipynb

## DBpedia

### Using pre-processed datasets

Download and unzip DBpedia.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace

    ./data/DBpedia/input/

### Dataset Construction

Download the following files: [*T*](http://downloads.dbpedia.org/2016-10/dbpedia_2016-10.nt),

[*Aee*](http://downloads.dbpedia.org/2016-10/core-i18n/en/mappingbased_objects_wkd_uris_en.ttl.bz2), and 

[*Aec*](http://downloads.dbpedia.org/2016-10/core-i18n/en/instance_types_transitive_wkd_uris_en.ttl.bz2)

Unzip the files to:

    ./data/DBpedia/raw/

Run all cells in:

    ./code/ppc_DBpedia/raw2mid.ipynb

    ./code/ppc_DBpedia/ppc.ipynb

## Gene Ontology (GO)

### Using pre-processed datasets

Download and unzip GO.zip from [here](https://drive.google.com/drive/folders/1g3_7v-Alzh5o6_3iowt9Auq_3Z916xjL?usp=share_link), and replace

    ./data/GO/input/

### Dataset Construction

Download the raw data [here](https://bio2vec.cbrc.kaust.edu.sa/data/elembeddings/el-embeddings-data.zip) and unzip it to:

    ./data/GO/raw/

Generate axioms using:

    groovy ./code/ppc_GO/GetOntology.groovy ./data/GO/raw/data-train/yeast-classes.owl > ./data/GO/raw/ontology.txt

Generate intermediate data using:

    cd ./code/ppc_GO/ && python raw2mid.py

Run all cells in:

    ./code/ppc_GO/ppc.ipynb

# Run

To reproduce the main results, simply run the following commands:

    python TAR.py --dataset YAGO4

    python TAR.py --dataset DBpedia

    python TAR.py --dataset GO