A python framework to transform natural language questions to queries in a database query language.

Related tags

Text Data & NLPquepy
Overview
  __ _ _   _  ___ _ __  _   _
 / _` | | | |/ _ \ '_ \| | | |
| (_| | |_| |  __/ |_) | |_| |
 \__, |\__,_|\___| .__/ \__, |
    |_|          |_|    |___/

What's quepy?

Quepy is a python framework to transform natural language questions to queries in a database query language. It can be easily customized to different kinds of questions in natural language and database queries. So, with little coding you can build your own system for natural language access to your database.

Currently Quepy provides support for Sparql and MQL query languages. We plan to extended it to other database query languages.

An example

To illustrate what can you do with quepy, we included an example application to access DBpedia contents via their sparql endpoint.

You can try the example online here: Online demo

Or, you can try the example yourself by doing:

python examples/dbpedia/main.py "Who is Tom Cruise?"

And it will output something like this:

SELECT DISTINCT ?x1 WHERE {
    ?x0 rdf:type foaf:Person.
    ?x0 rdfs:label "Tom Cruise"@en.
    ?x0 rdfs:comment ?x1.
}

Thomas Cruise Mapother IV, widely known as Tom Cruise, is an...

The transformation from natural language to sparql is done by first using a special form of regular expressions:

person_name = Group(Plus(Pos("NNP")), "person_name")
regex = Lemma("who") + Lemma("be") + person_name + Question(Pos("."))

And then using and a convenient way to express semantic relations:

person = IsPerson() + HasKeyword(person_name)
definition = DefinitionOf(person)

The rest of the transformation is handled automatically by the framework to finally produce this sparql:

SELECT DISTINCT ?x1 WHERE {
    ?x0 rdf:type foaf:Person.
    ?x0 rdfs:label "Tom Cruise"@en.
    ?x0 rdfs:comment ?x1.
}

Using a very similar procedure you could generate and MQL query for the same question obtaining:

[{
    "/common/topic/description": [{}],
    "/type/object/name": "Tom Cruise",
    "/type/object/type": "/people/person"
}]

Installation

You need to have installed docopt and numpy. Other than that, you can just type:

pip install quepy

You can get more details on the installation here:

http://quepy.readthedocs.org/en/latest/installation.html

Learn more

You can find a tutorial here:

http://quepy.readthedocs.org/en/latest/tutorial.html

And the full documentation here:

http://quepy.readthedocs.org/

Join our mailing list

Contribute!

Want to help develop quepy? Welcome aboard! Find us in http://groups.google.com/group/quepy

Owner
Machinalis
Machinalis
NL. The natural language programming language.

NL A Natural-Language programming language. Built using Codex. A few examples are inside the nl_projects directory. How it works Write any code in pur

2 Jan 17, 2022
天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

zxx飞翔的鱼 751 Dec 30, 2022
A Fast Command Analyser based on Dict and Pydantic

Alconna Alconna 隶属于ArcletProject, 在Cesloi内有内置 Alconna 是 Cesloi-CommandAnalysis 的高级版,支持解析消息链 一般情况下请当作简易的消息链解析器/命令解析器 文档 暂时的文档 Example from arclet.alcon

19 Jan 03, 2023
Python powered crossword generator with database with 20k+ polish words

crossword_generator Generate simple crossword puzzle from words and definitions fetched from krzyżowki.edu.pl endpoints -/ string:word - returns js

0 Jan 04, 2022
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
ProteinBERT is a universal protein language model pretrained on ~106M proteins from the UniRef90 dataset.

ProteinBERT is a universal protein language model pretrained on ~106M proteins from the UniRef90 dataset. Through its Python API, the pretrained model can be fine-tuned on any protein-related task in

241 Jan 04, 2023
Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Yoon Kim 43 Dec 23, 2022
Th2En & Th2Zh: The large-scale datasets for Thai text cross-lingual summarization

Th2En & Th2Zh: The large-scale datasets for Thai text cross-lingual summarization 📥 Download Datasets 📥 Download Trained Models INTRODUCTION TH2ZH (

Nakhun Chumpolsathien 5 Jan 03, 2022
Large-scale Knowledge Graph Construction with Prompting

Large-scale Knowledge Graph Construction with Prompting across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)

ZJUNLP 161 Dec 28, 2022
A library for Multilingual Unsupervised or Supervised word Embeddings

MUSE: Multilingual Unsupervised and Supervised Embeddings MUSE is a Python library for multilingual word embeddings, whose goal is to provide the comm

Facebook Research 3k Jan 06, 2023
Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Depenencies

PyStanfordDependencies Python interface for converting Penn Treebank trees to Universal Dependencies and Stanford Dependencies. Example usage Start by

David McClosky 64 May 08, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

🤗 Contributing to OpenSpeech 🤗 OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform ta

Openspeech TEAM 513 Jan 03, 2023
SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering.

SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering. Contents Inst

0 Oct 21, 2021
Indobenchmark are collections of Natural Language Understanding (IndoNLU) and Natural Language Generation (IndoNLG)

Indobenchmark Toolkit Indobenchmark are collections of Natural Language Understanding (IndoNLU) and Natural Language Generation (IndoNLG) resources fo

Samuel Cahyawijaya 11 Aug 26, 2022
The FinQA dataset from paper: FinQA: A Dataset of Numerical Reasoning over Financial Data

Data and code for EMNLP 2021 paper "FinQA: A Dataset of Numerical Reasoning over Financial Data"

Zhiyu Chen 114 Dec 29, 2022
Binaural Speech Synthesis

Binaural Speech Synthesis This repository contains code to train a mono-to-binaural neural sound renderer. If you use this code or the provided datase

Facebook Research 135 Dec 18, 2022
IMS-Toucan is a toolkit to train state-of-the-art Speech Synthesis models

IMS-Toucan is a toolkit to train state-of-the-art Speech Synthesis models. Everything is pure Python and PyTorch based to keep it as simple and beginner-friendly, yet powerful as possible.

Digital Phonetics at the University of Stuttgart 247 Jan 05, 2023
Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

1.3k Dec 26, 2022
🕹 An esoteric language designed so that the program looks like the transcript of a Pokémon battle

PokéBattle is an esoteric language designed so that the program looks like the transcript of a Pokémon battle. Original inspiration and specification

Eduardo Correia 9 Jan 11, 2022