As the number of open knowledge graphs (KGs) growths, the complexity for users to find their way with data increases. Thus, it is important to provide to users approaches that help them handling KGs while writing queries. Several works demonstrated the importance to consider identity links (owl:sameAs) between entities as context-dependant identity links. W.r.t. an identity context, some properties might be propagated and some don't. In this work, we propose an approach based on sentence embedding to find those propagable properties for a given context.

Currently, only Wikipedia is supported due to specifities of its data model.

You must ensure the following requirements are met on your test machine:

• Python 3.6
• PyTorch
• Then install the following pip package hdt, numpy and nltk.

pip install numpy hdt nltk

To make InferSent work, make sure you have the NLTK tokenizer by running the following once:

import nltk
nltk.download('punkt')

and in dataset directory

mkdir GloVe
curl -Lo GloVe/glove.840B.300d.zip http://nlp.stanford.edu/data/glove.840B.300d.zip
unzip GloVe/glove.840B.300d.zip -d GloVe/
mkdir fastText
curl -Lo fastText/crawl-300d-2M.vec.zip https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip
unzip fastText/crawl-300d-2M.vec.zip -d fastText/

and

curl -Lo encoder/infersent1.pkl https://dl.fbaipublicfiles.com/infersent/infersent1.pkl
curl -Lo encoder/infersent2.pkl https://dl.fbaipublicfiles.com/infersent/infersent2.p

Finally, from rdfhdt.org get Wikidata HDT file:

curl -Lo dataset/wikidata2018_09_11.hdt.gz http://gaia.infor.uva.es/hdt/wikidata/wikidata2018_09_11.hdt.gz
gunzip dataset/wikidata2018_09_11.hdt.gz
curl -Lo dataset/wikidata/wikidata2018_09_11.hdt.index.v1-1 http://gaia.infor.uva.es/hdt/wikidata/wikidata2018_09_11.hdt.index.v1-1 
gunzip dataset/wikidata2018_09_11.hdt.index.v1-1.gz

To create an identity lattice:

python3 -m conproknow lattice --resource "http://www.wikidata.org/entity/Q90" --output /output_dir/  --hdt /path/to/wikidata2018_09_11.hdt

The precision:

The recall: