A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

Related tags

Text Data & NLPKGEval
Overview

KGEval

A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

The framework and experimental results are described in Ben Rim et al. 2021 (Outstanding Paper Award, AKBC 2021).

Instructions

Create a virtual environment

virtualenv -p python3.6 eval_env
source eval_env/bin/activate
pip install -r requirements.txt

Download data

In the main folder, run:

source data/download.sh

Download model

If you want to test the framework immediately, you can download pre-trained Pykeen models by running:

source download_models.sh

Generate behavioral tests

Symmetry Tests

Can choose --dataset FB15K237, WN18RR, YAGO310

python tests/run.py --dataset FB15K237 --mode generate --capability symmetry

This should result into the following output, and the files for each test set will be added under behavioral_tests\dataset\symmetry:

2021-10-03 23:37:35,060 - [INFO] - Preparing test sets for the dataset FB15K237
2021-10-03 23:37:37,621 - [INFO] - ########################## <----TRAIN---> ############################
2021-10-03 23:37:37,621 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,621 - [INFO] - 272115 triples remaining in train set
2021-10-03 23:37:37,621 - [INFO] - 6778 symmetric triples found in train set
2021-10-03 23:37:37,786 - [INFO] - ########################## <----TEST---> ############################
2021-10-03 23:37:37,786 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,786 - [INFO] - 20466 triples remaining in test set
2021-10-03 23:37:37,786 - [INFO] - 113 symmetric triples found in test set
2021-10-03 23:37:37,806 - [INFO] - ########################## <----VALID---> ############################
2021-10-03 23:37:37,806 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,806 - [INFO] - 17535 triples remaining in valid set
2021-10-03 23:37:37,806 - [INFO] - 113 symmetric triples found in valid set
2021-10-03 23:37:39,106 - [INFO] - #################### <---TEST SET 1: MEMORIZATION ---> ##########################
2021-10-03 23:37:39,106 - [INFO] - There are 5470 entries in the memorization set (occur in both directions)
2021-10-03 23:37:39,106 - [INFO] - #################### <---TEST SET 2: ONE DIRECTION SEEN ---> ##########################
2021-10-03 23:37:39,106 - [INFO] - There are 1308 entries not shown in both directions (to be reversed for testing)
2021-10-03 23:37:39,836 - [INFO] - #################### <--- SYMMETRIC RELATIONS ---> ##########################
2021-10-03 23:37:39,836 - [INFO] - TRAIN SET contains 6778 symmetric entries
2021-10-03 23:37:39,836 - [INFO] - TEST SET contains  113 symmetric entries with 113 not in training
2021-10-03 23:37:39,836 - [INFO] - VALID SET contains 113 symmetric entries with 113 not in training
2021-10-03 23:37:39,839 - [INFO] - #################### <---TEST SET 3: UNSEEN INSTANCES ---> ##########################
2021-10-03 23:37:39,840 - [INFO] - There are 226 entries that are not seen in any direction in training
2021-10-03 23:37:40,267 - [INFO] - #################### <---TEST SET 4: ASYMMETRY ---> ##########################
2021-10-03 23:37:40,267 - [INFO] - There are 3000 asymmetric entries in test set added to test 4

Hierarchy Tests

Only available for FB15K237 dataset

python tests/run.py --dataset FB15K237 --mode generate --capability hierarchy

The output should be and will be available under behavioral_tests/dataset/hierarchy/, the naming of the files corresponds to triples where the tail belongs to a specified level. For example, 1.txt contains triples where the tail has a type of level 1 in the entity type hierarchy :

2021-10-04 01:38:13,517 - [INFO] - Results of Hierarchy Behavioral Tests for FB15K237
2021-10-04 01:38:20,367 - [INFO] - <--------------- Entity Hiararchy statistics ----------------->
2021-10-04 01:38:20,568 - [INFO] - Level 0 contains 1 types and 3415 triples
2021-10-04 01:38:20,887 - [INFO] - Level 1 contains 66 types and 2006 triples
2021-10-04 01:38:20,900 - [INFO] - Level 2 contains 136 types and 4273 triples
2021-10-04 01:38:20,913 - [INFO] - Level 3 contains 213 types and 3560 triples
2021-10-04 01:38:20,923 - [INFO] - Level 4 contains 262 types and 3369 triples

Run Tests (pykeen models)

Symmetry behavioral tests on distmult or rotate:

python tests/run.py --dataset FB15K237 --mode test --model_name rotate

The output will be printed as shown below, and will also be available in the results folder under dataset/symmetry:

2021-10-04 14:00:57,100 - [INFO] - Starting test1 with rotate model
2021-10-04 14:03:23,249 - [INFO] - On test1, MR: 1.2407678244972578, MRR: 0.9400152688974949, [email protected]: 0.9014624953269958, [email protected]: 0.988482654094696, [email protected]: 0.9965264797210693
2021-10-04 14:03:23,249 - [INFO] - Starting test2 with rotate model
2021-10-04 14:04:15,614 - [INFO] - On test2, MR: 23.446483180428135, MRR: 0.4409348919640765, [email protected]: 0.30351680517196655, [email protected]: 0.5894495248794556, [email protected]: 0.7025994062423706
2021-10-04 14:04:15,614 - [INFO] - Starting test3 with rotate model
2021-10-04 14:04:25,364 - [INFO] - On test3, MR: 1018.9469026548672, MRR: 0.04786047740344238, [email protected]: 0.008849557489156723, [email protected]: 0.06194690242409706, [email protected]: 0.12389380484819412
2021-10-04 14:04:25,365 - [INFO] - Starting test4 with rotate model
2021-10-04 14:05:38,900 - [INFO] - On test4, MR: 4901.459, MRR: 0.07606098649786266, [email protected]: 0.9496666789054871, [email protected]: 0.893666684627533, [email protected]: 0.8823333382606506

Hierarchy behavioral tests on distmult or rotate:

   python tests/run.py --dataset FB15K237 --mode test --capability hierarchy --model_name rotate

Run Tests on other models and other frameworks

(To be added)

Owner
NEC Laboratories Europe
Research software developed at NEC Laboratories Europe
NEC Laboratories Europe
Multi Task Vision and Language

12-in-1: Multi-Task Vision and Language Representation Learning Please cite the following if you use this code. Code and pre-trained models for 12-in-

Meta Research 711 Jan 08, 2023
🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.

pySBD: Python Sentence Boundary Disambiguation (SBD) pySBD - python Sentence Boundary Disambiguation (SBD) - is a rule-based sentence boundary detecti

Nipun Sadvilkar 549 Jan 06, 2023
Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

PythonTextObfuscator Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense. Requi

2 Aug 29, 2022
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

fastNLP fastNLP是一款轻量级的自然语言处理(NLP)工具包,目标是快速实现NLP任务以及构建复杂模型。 fastNLP具有如下的特性: 统一的Tabular式数据容器,简化数据预处理过程; 内置多种数据集的Loader和Pipe,省去预处理代码; 各种方便的NLP工具,例如Embedd

fastNLP 2.8k Jan 01, 2023
An ActivityWatch watcher to pose questions to the user and record her answers.

aw-watcher-ask An ActivityWatch watcher to pose questions to the user and record her answers. This watcher uses Zenity to present dialog boxes to the

Bernardo Chrispim Baron 33 Dec 03, 2022
edge-SR: Super-Resolution For The Masses

edge-SR: Super Resolution For The Masses Citation Pablo Navarrete Michelini, Yunhua Lu and Xingqun Jiang. "edge-SR: Super-Resolution For The Masses",

Pablo 40 Nov 10, 2022
Header-only C++ HNSW implementation with python bindings

Hnswlib - fast approximate nearest neighbor search Header-only C++ HNSW implementation with python bindings. NEWS: version 0.6 Thanks to (@dyashuni) h

2.3k Jan 05, 2023
Chinese Named Entity Recognization (BiLSTM with PyTorch)

BiLSTM-CRF for Name Entity Recognition PyTorch version A PyTorch implemention of Bi-LSTM-CRF model for Chinese Named Entity Recognition. 使用 PyTorch 实现

5 Jun 01, 2022
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡

Translations 🇩🇪 DE 🇫🇷 FR 🇭🇺 HU 🇮🇩 ID 🇮🇹 IT 🇳🇱 NL 🇧🇷 PT-BR 🇷🇺 RU 🇨🇳 ZH ➡️ Documentation | Discord | Installation Guide ⬅️ Fully autom

11.2k Jan 05, 2023
NLP Text Classification

多标签文本分类任务 近年来随着深度学习的发展,模型参数的数量飞速增长。为了训练这些参数,需要更大的数据集来避免过拟合。然而,对于大部分NLP任务来说,构建大规模的标注数据集非常困难(成本过高),特别是对于句法和语义相关的任务。相比之下,大规模的未标注语料库的构建则相对容易。为了利用这些数据,我们可以

Jason 1 Nov 11, 2021
End-to-end MLOps pipeline of a BERT model for emotion classification.

image source EmoBERT-MLOps The goal of this repository is to build an end-to-end MLOps pipeline based on the MLOps course from Made with ML, but this

Dimitre Oliveira 4 Nov 06, 2022
Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Ubiquitous Knowledge Processing Lab 59 Dec 01, 2022
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 03, 2023
Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

ThreatFox2Misp Creating a Feed of MISP Events from ThreatFox (by abuse.ch) What will it do? This will fetch IOCs from ThreatFox by Abuse.ch, convert t

17 Nov 22, 2022
無料で使える中品質なテキスト読み上げソフトウェア、VOICEVOXの音声合成エンジン

VOICEVOX ENGINE VOICEVOXの音声合成エンジン。 実態は HTTP サーバーなので、リクエストを送信すればテキスト音声合成できます。 API ドキュメント VOICEVOX ソフトウェアを起動した状態で、ブラウザから

Hiroshiba 3 Jul 05, 2022
PIZZA - a task-oriented semantic parsing dataset

The PIZZA dataset continues the exploration of task-oriented parsing by introducing a new dataset for parsing pizza and drink orders, whose semantics cannot be captured by flat slots and intents.

17 Dec 14, 2022
Twitter-Sentiment-Analysis - Analysis of twitter posts' positive and negative score.

Twitter-Sentiment-Analysis The hands-on project is in Python 3 Programming class offered by University of Michigan via Coursera. The task is to build

Eszter Pai 1 Jan 03, 2022
Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any language

Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any

Little Endian 1 Apr 28, 2022
Understanding the Difficulty of Training Transformers

Admin Understanding the Difficulty of Training Transformers Guided by our analyses, we propose Adaptive Model Initialization (Admin), which successful

Liyuan Liu 300 Dec 29, 2022