Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

GEEER

Code supporting the paper Graph-Embedding Empowered Entity Retrieval

This repository contains resources developed within the following paper:

Graph-Embedding Empowered Entity Retrieval, Emma Gerritse, Faegheh Hasibi and Arjen de Vries

This repository is structured in the following way:

  • Code/ : Contains the code for computing scores (entity_score.py), a notebook for the visualisation (Embedding_quality.ipynb), and two scripts for scoring (rankscore.sh and ranklib_to_trec.py)
  • Data/ : Contains the linked entities used and the wikipedia redirects used
  • Runs/ : Contains all the runs used in the paper

Requirements

Running the code requires Python 3.

Embeddings

If one simply wants to download the embeddings, they can be accessed:

Wikipedia2vec embeddings with graph component, result of the following command:

wikipedia2vec train --min-entity-count 0 --disambi enwiki-20190701-pages-articles-multistream.xml.bz2 wikipedia2vec_trained 

and Wikipedia2vec embeddings without graph component , result of the following command:

wikipedia2vec train --min-entity-count 0 --disambi --no-link-graph enwiki-20190701-pages-articles-multistream.xml.bz2 wikipedia2vec_trained 

The embeddings can then be loaded in Python with Gensim:

import gensim
model = gensim.models.KeyedVectors.load("WKN-vectors.bin", mmap='r')

Quickstart

To download all the auxilary files (Ranklib, DBpedia Entity V2 and the embeddings), please use the following command:

bash build.sh

To then reproduce the results, first make sure to install all the neccessary packages with:

pip install -r requirements.txt

And then run

bash Code/reproduce.sh

The results will be stored in the folder /Output

Code for computing the embedding based score

To compute just the embedding based score, use the following function:

python Code/entity_score.py embeddingfile outputfile [pathtodbpedia]
python Code/entity_score.py src/WKN-vectors/WKN-vectors.bin output.txt src/DBpedia-Entity/runs/v2/bm25f-ca_v2.run

Code for scoring with Ranklib

If you want to run Ranklib with 5 folds afterwards, use the following function:

python Code/entity_score_folds.py embeddingfile outputfolder outputfile [pathtodbpedia]

So for example

python Code/entity_score_folds.py src/WKN-vectors/WKN-vectors.bin Outputfolder output.txt src/DBpedia-Entity/runs/v2/bm25f-ca_v2.run

To do the coordinate ascent and ranking of these files, please run the following script with the Outputfolder from the previous line:

bash Code/train_ranklib.sh Outputfolder

bash Code/score_ranklib.sh Outputfolder

The first script will train Ranklib, and the second script will score according to Ranklib and will result in the ranking and the trec_eval scores of the ranking.

Cite

@inproceedings{Gerritse:2020:GEEER, 
   author =    {Gerritse, Emma and Hasibi, Faegheh and De Vries, Arjen},
   title =     {Graph-Embedding Empowered Entity Retrieval},
   booktitle={European Conference on Information Retrieval},
   series =    {ECIR '20},
   year =      {2020},
   publisher = {Springer},
} 

Contact

If you have any questions, please contact Emma Gerritse at emma.gerritse@ru.nl

About

Code supporting the paper Graph-Embedding Empowered Entity Retrieval

Resources

Stars

Watchers

Forks

Releases

No releases published