https://github.com/uma-pi1/minie
An open information extraction system that provides compact extractions
https://github.com/uma-pi1/minie
extract-information information-extraction natural-language-processing natural-language-understanding nlp-apis nlp-library nlp-resources open-information-extraction paper
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An open information extraction system that provides compact extractions
- Host: GitHub
- URL: https://github.com/uma-pi1/minie
- Owner: uma-pi1
- License: gpl-3.0
- Created: 2017-07-06T11:02:25.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-02-26T01:08:19.000Z (almost 4 years ago)
- Last Synced: 2025-10-09T01:34:43.110Z (3 months ago)
- Topics: extract-information, information-extraction, natural-language-processing, natural-language-understanding, nlp-apis, nlp-library, nlp-resources, open-information-extraction, paper
- Language: Java
- Homepage: https://www.uni-mannheim.de/dws/research/resources/minie/
- Size: 4.48 MB
- Stars: 93
- Watchers: 5
- Forks: 28
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-java - Minie
README
# MinIE: Open Information Extraction system
* [Open Information Extraction - brief introduction](#open-information-extraction---brief-introduction)
* [MinIE - Open Information Extraction system](#minie---open-information-extraction-system)
* [Version](#version)
* [Demo](#demo)
* [MinIE service](#minie-service)
* [Python wrapper](#python-wrapper)
* [Resources](#resources)
* [MinIE in other downstream applications](#minie-in-other-downstream-applications)
* [Citing](#citing)
---
## Open Information Extraction - brief introduction
Open Information Extraction (OIE) systems aim to extract unseen relations and their arguments from unstructured text in unsupervised manner. In its simplest form, given a natural language sentence, they extract information in the form of a triple, consisted of subject (S), relation (R) and object (O).
Suppose we have the following input sentence:
```
AMD, which is based in U.S., is a technology company.
```
An OIE system aims to make the following extractions:
```
("AMD"; "is based in"; "U.S.")
("AMD"; "is"; "technology company")
```
## MinIE - Open Information Extraction system
An Open Information Extraction system, providing useful extractions:
* represents contextual information with semantic annotations
* identifies and removes words that are considered overly specific
* high precision/recall
* shorter, semantically enriched extractions
## Version
This is the latest version of MinIE, which may give you different (improved!) results than the original EMNLP-2017 version. The EMNLP-2017 version can be found [here](https://github.com/uma-pi1/minie/tree/5500282a4edd213910ecbcd95a94fca86a057e2d).
## Demo
In general, the code for running MinIE in all of its modes is almost the same. The only exception is MinIE-D, which requires additional input (list of multi-word dictionaries). You can still use MinIE-D without providing multi-word dictionaries, but then MinIE-D assumes that you provided an empty dictionary, thus minimizing all the words which are candidates for dropping.
The following code demo is for MinIE-S (note that you can use the same for the rest of the modes, you just need to change `MinIE.Mode` accordingly):
```java
import de.uni_mannheim.minie.MinIE;
import de.uni_mannheim.minie.annotation.AnnotatedProposition;
import de.uni_mannheim.utils.coreNLP.CoreNLPUtils;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
public class Demo {
public static void main(String args[]) {
// Dependency parsing pipeline initialization
StanfordCoreNLP parser = CoreNLPUtils.StanfordDepNNParser();
// Input sentence
String sentence = "The Joker believes that the hero Batman was not actually born in
foggy Gotham City.";
// Generate the extractions (With SAFE mode)
MinIE minie = new MinIE(sentence, parser, MinIE.Mode.SAFE);
// Print the extractions
System.out.println("\nInput sentence: " + sentence);
System.out.println("=============================");
System.out.println("Extractions:");
for (AnnotatedProposition ap: minie.getPropositions()) {
System.out.println("\tTriple: " + ap.getTripleAsString());
System.out.print("\tFactuality: " + ap.getFactualityAsString());
if (ap.getAttribution().getAttributionPhrase() != null)
System.out.print("\tAttribution: " + ap.getAttribution().toStringCompact());
else
System.out.print("\tAttribution: NONE");
System.out.println("\n\t----------");
}
System.out.println("\n\nDONE!");
}
}
```
If you want to use MinIE-D, then the only difference would be the way MinIE is called:
```java
import de.uni_mannheim.utils.Dictionary;
. . .
// Initialize dictionaries
String [] filenames = new String [] {"/minie-resources/wiki-freq-args-mw.txt",
"/minie-resources/wiki-freq-rels-mw.txt"};
Dictionary collocationsDict = new Dictionary(filenames);
// Use MinIE
MinIE minie = new MinIE(sentence, parser, MinIE.Mode.DICTIONARY, collocationsDict);
```
In `resources/minie-resources/` you can find multi-word dictionaries constructed from WordNet (wn-mwe.txt) and from wiktionary (wiktionary-mw-titles.txt). This will give you some sort of functionality for MinIE-D. The multi-word dictionaries constructed with MinIE-S (as explained in the paper) are not here because of their size. If you want to use them, please refer to the download link in the section "Resources".
## MinIE Service
Code for exposing MinIE as a service (developed by [Pasquale Minervini](https://github.com/pminervini)).
Start with:
```bash
$ mvn clean compile exec:java
[..]
[INFO] --- exec-maven-plugin:1.6.0:java (default-cli) @ minie-service ---
MinIE Service
Mar 06, 2018 8:43:13 PM org.glassfish.grizzly.http.server.NetworkListener start
INFO: Started listener bound to [localhost:8080]
Mar 06, 2018 8:43:13 PM org.glassfish.grizzly.http.server.HttpServer start
INFO: [HttpServer] Started.
Application started.
Stop the application using CTRL+C
```
Use the service with:
```bash
$ curl 'http://localhost:8080/minie/query' -X POST -d 'Obama visited the white house.' | jq .
{
"facts": [
{
"subject": "Obama",
"predicate": "visited",
"object": "white house"
}
]
}
```
## Python wrapper
You can find a python wrapper for MinIE [here](https://github.com/mmxgn/miniepy). If you want to use MinIE with python, please follow the guidelines provided on the repo's README.
## Resources
* **Documentation:** for more thorough documentation for the code, please visit [MinIE's project page](https://uma-pi1.github.io/minie/).
* **Paper:** _"MinIE: Minimizing Facts in Open Information Extraction"_ - appeared on EMNLP 2017 [[pdf]](http://aclweb.org/anthology/D/D17/D17-1278.pdf)
* **Dictionary:** Wikipedia: frequent relations and arguments [[zip]](https://www.uni-mannheim.de/media/Einrichtungen/dws/Files_Research/Software/MinIE/wiki-freq-args-rels.zip)
* **Experiments datasets:** datasets from the paper
* [NYT](https://www.uni-mannheim.de/media/Einrichtungen/dws/Files_Research/Software/MinIE/NYT.zip)
* [Wiki](https://www.uni-mannheim.de/media/Einrichtungen/dws/Files_Research/Software/MinIE/Wiki.zip)
* [NYT-10k](https://www.uni-mannheim.de/media/Einrichtungen/dws/Files_Research/Software/MinIE/nyt10k.zip)
## MinIE in other downstream applications
* **Fact Salience:** MinIE is used for the task of "Fact Salience". Details can be found in the paper [*"Facts that Matter"*](http://www.aclweb.org/anthology/D18-1129) by Marco Ponza, Luciano Del Corro, Gerhard Weikum, published on EMNLP 2018. As a result, the fact salience system [SalIE](https://github.com/mponza/SalIE) was published.
* **Large-Scale OIE:** MinIE was used to create the largest OIE corpus to date - [OPIEC](https://www.uni-mannheim.de/dws/research/resources/opiec/). The corpus contains more than 341M triples. Details can be found in the paper [*"OPIEC: An Open Information Extraction Corpus"*](https://arxiv.org/pdf/1904.12324.pdf) by Kiril Gashteovski, Sebastian Wanner, Sven Hertling, Samuel Broscheit, Rainer Gemulla, published on AKBC 2019.
* **OIE from Scientific Publications:** An extension of MinIE was created which provides structured knowledge enriched with semantic information about citations - [MinScIE: Citation-centered Open Information Extraction](https://github.com/gkiril/MinSCIE). Details can be found in the paper [*"MinScIE: Citation-centered Open Information Extraction"*](https://madoc.bib.uni-mannheim.de/49216/1/_JCDL19Demo__MinScIE%20%284%29.pdf) by Anne Lauscher, Yide Song and Kiril Gashteovski, published on JCDL 2019.
* **Entity Aspect Linking:** MinIE was used for creating EAL: toolkit and dataset for entity-aspect linking. Details can be found in the paper [*"EAL: A Toolkit and Dataset for Entity-Aspect Linking"*](https://madoc.bib.uni-mannheim.de/49596/1/EAL.pdf) by Federico Nanni, Jingyi Zhang, Ferdinand Betz, Kiril Gashteovski, published on JCDL 2019.
## Citing
If you use MinIE in your work, please cite our paper:
```
@inproceedings{gashteovski2017minie,
title={MinIE: Minimizing Facts in Open Information Extraction},
author={Gashteovski, Kiril and Gemulla, Rainer and Del Corro, Luciano},
booktitle={Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
pages={2630--2640},
year={2017}
}
```