KGReasoning

This repo contains several algorithms for multi-hop reasoning on knowledge graphs, including the official Pytorch implementation of Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs.

Models

• BetaE
• Query2box
• GQE

KG Data

The KG data (FB15k, FB15k-237, NELL995) mentioned in the BetaE paper and the Query2box paper can be downloaded here. Note the two use the same training queries, but the difference is that the valid/test queries in BetaE paper have a maximum number of answers, making it more realistic.

Each folder in the data represents a KG, including the following files.

• train.txt/valid.txt/test.txt: KG edges
• id2rel/rel2id/ent2id/id2ent.pkl: KG entity relation dicts
• train-queries/valid-queries/test-queries.pkl: defaultdict(set), each key represents a query structure, and the value represents the instantiated queries
• train-answers.pkl: defaultdict(set), each key represents a query, and the value represents the answers obtained in the training graph (edges in train.txt)
• valid-easy-answers/test-easy-answers.pkl: defaultdict(set), each key represents a query, and the value represents the answers obtained in the training graph (edges in train.txt) / valid graph (edges in train.txt+valid.txt)
• valid-hard-answers/test-hard-answers.pkl: defaultdict(set), each key represents a query, and the value represents the additional answers obtained in the validation graph (edges in train.txt+valid.txt) / test graph (edges in train.txt+valid.txt+test.txt)

We represent the query structures using a tuple in case we run out of names :), (credits to @michiyasunaga). For example, 1p queries: (e, (r,)) and 2i queries: ((e, (r,)),(e, (r,))). Check the code for more details.

Examples

Please refer to the examples.sh for the scripts of all 3 models on all 3 datasets.

Citations

If you use this repo, please cite the following paper.

@inproceedings{
 ren2020beta,
 title={Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs},
 author={Hongyu Ren and Jure Leskovec},
 booktitle={Neural Information Processing Systems},
 year={2020}
}