Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Related tags

Deep Learningcqd
Overview

Continuous Query Decomposition

This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neural Link Predictors:

@inproceedings{
    arakelyan2021complex,
    title={Complex Query Answering with Neural Link Predictors},
    author={Erik Arakelyan and Daniel Daza and Pasquale Minervini and Michael Cochez},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=Mos9F9kDwkz}
}

In this work we present CQD, a method that reuses a pretrained link predictor to answer complex queries, by scoring atom predicates independently and aggregating the scores via t-norms and t-conorms.

Our code is based on an implementation of ComplEx-N3 available here.

Please follow the instructions next to reproduce the results in our experiments.

1. Install the requirements

We recommend creating a new environment:

% conda create --name cqd python=3.8 && conda activate cqd
% pip install -r requirements.txt

2. Download the data

We use 3 knowledge graphs: FB15k, FB15k-237, and NELL. From the root of the repository, download and extract the files to obtain the folder data, containing the sets of triples and queries for each graph.

% wget http://data.neuralnoise.com/cqd-data.tgz
% tar xvf cqd-data.tgz

3. Download the models

Then you need neural link prediction models -- one for each of the datasets. Our pre-trained neural link prediction models are available here:

% wget http://data.neuralnoise.com/cqd-models.tgz
% tar xvf cqd-data.tgz

3. Alternative -- Train your own models

To obtain entity and relation embeddings, we use ComplEx. Use the next commands to train the embeddings for each dataset.

FB15k

% python -m kbc.learn data/FB15k --rank 1000 --reg 0.01 --max_epochs 100  --batch_size 100

FB15k-237

% python -m kbc.learn data/FB15k-237 --rank 1000 --reg 0.05 --max_epochs 100  --batch_size 1000

NELL

% python -m kbc.learn data/NELL --rank 1000 --reg 0.05 --max_epochs 100  --batch_size 1000

Once training is done, the models will be saved in the models directory.

4. Answering queries with CQD

CQD can answer complex queries via continuous (CQD-CO) or combinatorial optimisation (CQD-Beam).

CQD-Beam

Use the kbc.cqd_beam script to answer queries, providing the path to the dataset, and the saved link predictor trained in the previous step. For example,

% python -m kbc.cqd_beam --model_path models/[model_filename].pt

Example:

% PYTHONPATH=. python3 kbc/cqd_beam.py \
  --model_path models/FB15k-model-rank-1000-epoch-100-*.pt \
  --dataset FB15K --mode test --t_norm product --candidates 64 \
  --scores_normalize 0 data/FB15k

models/FB15k-model-rank-1000-epoch-100-1602520745.pt FB15k product 64
ComplEx(
  (embeddings): ModuleList(
    (0): Embedding(14951, 2000, sparse=True)
    (1): Embedding(2690, 2000, sparse=True)
  )
)

[..]

This will save a series of JSON fils with results, e.g.

% cat "topk_d=FB15k_t=product_e=2_2_rank=1000_k=64_sn=0.json"
{
  "MRRm_new": 0.7542805715523118,
  "MRm_new": 50.71081983144581,
  "[email protected]_new": 0.6896709378392843,
  "[email protected]_new": 0.7955001359095913,
  "[email protected]_new": 0.8676865172456019
}

CQD-CO

Use the kbc.cqd_co script to answer queries, providing the path to the dataset, and the saved link predictor trained in the previous step. For example,

% python -m kbc.cqd_co data/FB15k --model_path models/[model_filename].pt --chain_type 1_2

Final Results

All results from the paper can be produced as follows:

% cd results/topk
% ../topk-parse.py *.json | grep rank=1000
d=FB15K rank=1000 & 0.779 & 0.584 & 0.796 & 0.837 & 0.377 & 0.658 & 0.839 & 0.355
d=FB237 rank=1000 & 0.279 & 0.219 & 0.352 & 0.457 & 0.129 & 0.249 & 0.284 & 0.128
d=NELL rank=1000 & 0.343 & 0.297 & 0.410 & 0.529 & 0.168 & 0.283 & 0.536 & 0.157
% cd ../cont
% ../cont-parse.py *.json | grep rank=1000
d=FB15k rank=1000 & 0.454 & 0.191 & 0.796 & 0.837 & 0.336 & 0.513 & 0.816 & 0.319
d=FB15k-237 rank=1000 & 0.213 & 0.131 & 0.352 & 0.457 & 0.146 & 0.222 & 0.281 & 0.132
d=NELL rank=1000 & 0.265 & 0.220 & 0.410 & 0.529 & 0.196 & 0.302 & 0.531 & 0.194
Owner
UCL Natural Language Processing
UCL Natural Language Processing
ScaleNet: A Shallow Architecture for Scale Estimation

ScaleNet: A Shallow Architecture for Scale Estimation Repository for the code of ScaleNet paper: "ScaleNet: A Shallow Architecture for Scale Estimatio

Axel Barroso 34 Nov 09, 2022
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
DeepDiffusion: Unsupervised Learning of Retrieval-adapted Representations via Diffusion-based Ranking on Latent Feature Manifold

DeepDiffusion Introduction This repository provides the code of the DeepDiffusion algorithm for unsupervised learning of retrieval-adapted representat

4 Nov 15, 2022
An AI Assistant More Than a Toolkit

tymon An AI Assistant More Than a Toolkit The reason for creating framework tymon is simple. making AI more like an assistant, helping us to complete

TymonXie 46 Oct 24, 2022
Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

gts3.org (<a href=[email protected])"> 55 Oct 25, 2022
A cross-lingual COVID-19 fake news dataset

CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,

Yingtong Dou 11 Dec 01, 2022
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.

CaiT-TF (Going deeper with Image Transformers) This repository provides TensorFlow / Keras implementations of different CaiT [1] variants from Touvron

Sayak Paul 9 Jun 26, 2022
Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching

Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching This is our attempt of the shared task on Quan

Manav Nitin Kapadnis 12 Jul 08, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Nsdf: A mesh SDF with just some code we can directly paste into our raymarcher

nsdf Representing SDFs of arbitrary meshes has been a bit tricky so far. Express

Jan Ivanecky 5 Feb 18, 2022
[TIP 2020] Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion Code for Multi-Temporal Scene Classification and Scene Ch

Lixiang Ru 33 Dec 12, 2022
Spam your friends and famly and when you do your famly will disown you and you will have no friends.

SpamBot9000 Spam your friends and family and when you do your family will disown you and you will have no friends. Terms of Use Disclaimer: Please onl

DJ15 0 Jun 09, 2022
This tutorial repository is to introduce the functionality of KGTK to first-time users

Welcome to the KGTK notebook tutorial The goal of this tutorial repository is to introduce the functionality of KGTK to first-time users. The Knowledg

USC ISI I2 58 Dec 21, 2022
All public open-source implementations of convnets benchmarks

convnet-benchmarks Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below. Machine: 6-cor

Soumith Chintala 2.7k Dec 30, 2022
Rohit Ingole 2 Mar 24, 2022
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

75 Nov 24, 2022
A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

europilot Overview Europilot is an open source project that leverages the popular Euro Truck Simulator(ETS2) to develop self-driving algorithms. A con

1.4k Jan 04, 2023
Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

This repository is used to suspend the results of our paper "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement"

ScorpioMiku 19 Sep 30, 2022