README.md

Note:

In this repo, we present the application of CASE-BASED REASONING FOR DRUG REPURPOSING

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Code for the AKBC'20 paper -- A Simple Approach to Case-Based Reasoning in Knowledge Bases

Setup

Download the experiment data from here, untar it in the root directory of the project.

The compressed size is ~401M and uncompressed size is ~716M.

wget  http://iesl.cs.umass.edu/downloads/cbr-akbc-data/cbr-akbc-data.tar.gz && tar xvf cbr-akbc-data.tar.gz

Set the pythonpath in root directory of the project

export PYTHONPATH=`pwd`

To reproduce results from the paper, run the following:

WN18RR
python code/cbr.py --dataset_name WN18RR --test --k_adj 5 --max_num_programs 25

Sample output

[2020-06-30 01:48:46     Avg number of programs 22.41]
[2020-06-30 01:48:46     Avg number of answers after executing the programs: 88.83012354651163]
[2020-06-30 01:48:46     Accuracy (Loose): 0.6043395022335674]
[2020-06-30 01:48:46     Hits@1 0.3940650925335035]
[2020-06-30 01:48:46     Hits@3 0.4610721123165284]
[2020-06-30 01:48:46     Hits@5 0.4834077855775367]
[2020-06-30 01:48:46     Hits@10 0.5113273771537971]
[2020-06-30 01:48:46     MRR 0.4353462886214267]
[2020-06-30 01:48:46     Avg number of nn, that do not have the query relation: 0.0]
[2020-06-30 01:48:46     Avg num of returned nearest neighbors: 4.8146]
[2020-06-30 01:48:46     Avg number of programs that do not execute per query: 9.7958]

To run for a new dataset, first a random subgraph around each entity needs to be collected and written to disk.

In our experiments, we just collect 1000 random paths aorund each entity by doing DFS.

python code/data/get_paths.py --dataset_name <insert_dataset_name> --num_paths_to_collect 1000 --data_dir cbr_akbc_data

Citation

@inproceedings{cbr_akbc,
  title = {A Simple Approach to Case-Based Reasoning in Knowledge Bases},
  author = {Das, Rajarshi and Godbole, Ameya and Dhuliawala, Shehzaad and Zaheer, Manzil and McCallum, Andrew},
  booktitle = {AKBC},
  year = 2020
}

CBRonMRN