An Object Oriented Programming (OOP) interface for Ontology Web language (OWL) ontologies.

Overview

code GPLv3 license release

Enabling a developer to use Ontology Web Language (OWL) along with its reasoning capabilities in an Object Oriented Programming (OOP) paradigm, by providing an easy to use API, i.e., OWLOOP.

Although OWL and OOP paradigms have similar structure, there are some key differences between them; see this W3C publication for more details about the differences. Nonetheless, it is possible to use OWL along with its reasoning capabilities within applications developed in an OOP paradigm, by using the classic OWL-API. But, the usage of the classic OWL-API leaves your project with lots of boilerplate code. Therefore, the OWLOOP-API (built on top of OWL-API), reduces boilerplate code by enabling interaction with 'OWL entities' (i.e, Concept (also known as Class), Individual, Object property and Data property) as objects within the OOP paradigm. These objects are termed as Descriptors (i.e., ClassDescriptor, IndividualDescriptor, ObjectPropertyDescriptor and DataPropertyDescriptor). By using descriptor(s), OWLOOP synchronizes axioms (OWL2-DL axioms) between the OOP paradigm (your application's code) and the OWL paradigm (OWL ontology XML/RDF file(s)).

Example of a real-world system that used OWLOOP API:

This video (link) shows a smart home system recognising human activities. The system uses a network of multiple ontologies to recognise specific activities. The network of multiple ontologies was developed using OWLOOP API.

Table of Contents

  1. Reference to the publication
  2. Getting Started with OWLOOP
  3. Overview of important Java-classes (in OWLOOP) and their methods
  4. Wiki documentation
  5. Some details about OWLOOP dependencies
  6. Developers' message
  7. License

1. Reference to the Publication

OWLOOP API is a peer reviewed software published by Elsevier in its journal SoftwareX. The publication presents in detail the motivation for developing OWLOOP. Furthermore, it describes the design of the API and presents the API's usage with illustrative examples.

Please, cite this work as:

@article{OWLOOP-2021,
  title = {{OWLOOP}: {A} Modular {API} to Describe {OWL} Axioms in {OOP} Objects Hierarchies},
  author = {Luca Buoncompagni and Syed Yusha Kareem and Fulvio Mastrogiovanni},
  journal = {SoftwareX},
  volume = {17},
  pages = {100952},
  year = {2022},
  issn = {2352-7110},
  doi = {https://doi.org/10.1016/j.softx.2021.100952},
  url = {https://www.sciencedirect.com/science/article/pii/S2352711021001801}
}

2. Getting Started with OWLOOP

2.1. Prerequisites for your Operating System

2.2. Add OWLOOP dependencies to your project

First Step: Create a new project with Java as the programming language and Gradle as the build tool.

Second Step: Create a directory called lib and place the OWLOOP related jar files in it.

Third Step: Modify your build.gradle file, as follows:

  • Add flatDir { dirs 'lib' } within the repositories{} section, as shown below:
repositories {
    mavenCentral()

    flatDir {
        dirs 'lib'
    }
}
  • Add the required dependencies (i.e., owloop, amor and pellet), as shown below 👇
dependencies {
    // testCompile group: 'junit', name: 'junit', version: '4.12'

    implementation 'it.emarolab.amor:amor:2.2'
    implementation 'it.emarolab.owloop:owloop:2.1'
    implementation group: 'com.github.galigator.openllet', name: 'openllet-owlapi', version: '2.5.1'
}

It is normal that a warning like SLF4J: Class path contains multiple SLF4J bindings occurs.

Final Step: You are now ready to create/use OWL ontologies in your project/application 🔥 , by using OWLOOP descriptors in your code!.

2.3. Use OWLOOP in your project

  • This is an example code that shows how to create an OWL file and add axioms to it.
import it.emarolab.amor.owlInterface.OWLReferences;
import it.emarolab.owloop.core.Axiom;
import it.emarolab.owloop.descriptor.utility.classDescriptor.FullClassDesc;
import it.emarolab.owloop.descriptor.utility.individualDescriptor.FullIndividualDesc;
import it.emarolab.owloop.descriptor.utility.objectPropertyDescriptor.FullObjectPropertyDesc;

public class someClassInMyProject {

    public static void main(String[] args) {

        // Disabling 'internal logs' (so that our console is clean)
        Axiom.Descriptor.OntologyReference.activateAMORlogging(false);

        // Creating an object that is 'a reference to an ontology'
        OWLReferences ontoRef = Axiom.Descriptor.OntologyReference.newOWLReferencesCreatedWithPellet(
                "robotAtHomeOntology",
                "src/main/resources/robotAtHomeOntology.owl",
                "http://www.semanticweb.org/robotAtHomeOntology",
                true
        );

        // Creating some 'classes in the ontology'
        FullClassDesc location = new FullClassDesc("LOCATION", ontoRef);
        location.addSubClass("CORRIDOR");
        location.addSubClass("ROOM");
        location.writeAxioms();
        FullClassDesc robot = new FullClassDesc("ROBOT", ontoRef);
        robot.addDisjointClass("LOCATION");
        robot.writeAxioms();

        // Creating some 'object properties in the ontology'
        FullObjectPropertyDesc isIn = new FullObjectPropertyDesc("isIn", ontoRef);
        isIn.addDomainClassRestriction("ROBOT");
        isIn.addRangeClassRestriction("LOCATION");
        isIn.writeAxioms();
        FullObjectPropertyDesc isLinkedTo = new FullObjectPropertyDesc("isLinkedTo", ontoRef);
        isLinkedTo.addDomainClassRestriction("CORRIDOR");
        isLinkedTo.addRangeClassRestriction("ROOM");
        isLinkedTo.writeAxioms();

        // Creating some 'individuals in the ontology'
        FullIndividualDesc corridor1 = new FullIndividualDesc("Corridor1", ontoRef);
        corridor1.addObject("isLinkedTo", "Room1");
        corridor1.addObject("isLinkedTo", "Room2");
        corridor1.writeAxioms();
        FullIndividualDesc robot1 = new FullIndividualDesc("Robot1", ontoRef);
        robot1.addObject("isIn", "Room1");
        robot1.writeAxioms();
        
        // Saving axioms from in-memory ontology to the the OWL file located in 'src/main/resources'
        ontoRef.saveOntology();
    }
}
  • After running the above code, the OWL file robotAtHomeOntology gets saved in src/main/resources. We can open the OWL file in Protege and view the ontology.

3. Overview of important Java-classes (in OWLOOP) and their methods

Java-classes methods
Path: OWLOOP/src/.../owloop/core/

This path contains, all core Java-classes. Among them, one in particular is immediately useful, i.e., OntologyReference. It allows to create/load/save an OWL ontology file.
The following method allows to enable/disable display of internal logging:

activateAMORlogging()
The following methods allow to instantiate an object of the Java-class OWLReferences:

newOWLReferencesCreatedWithPellet()
newOWLReferencesFromFileWithPellet()
newOWLReferencesFromWebWithPellet()
The object of Java-class OWLReferences, offers the following methods:

#0000FFsaveOntology()
#0000FFsynchronizeReasoner()
#0000FFload() // is hidden and used internally
Path: OWLOOP/src/.../owloop/descriptor/utility/

This path contains the directories that contain all Java-classes that are (as we call them) descriptors. The directories are the following:
/classDescriptor
/dataPropertyDescriptor
/objectPropertyDescriptor
/individualDescriptor.
The object of a Descriptor, offers the following methods:

#f03c15add...()
#f03c15remove...()
#f03c15build...()
#f03c15get...()
#f03c15query...()
#f03c15writeAxioms()
#f03c15readAxioms()
#f03c15reason()
#f03c15saveOntology()

4. Wiki documentation

The OWLOOP API's core aspects are described in this repository's wiki:

  • Structure of the OWLOOP API project.

  • JavaDoc of the OWLOOP API project.

  • What is a Descriptor in OWLOOP?

  • Code examples that show how to:

    • Construct a type of descriptor.

    • Add axioms to an ontology by using descriptors.

    • Infer some knowledge (i.e., axioms) from the axioms already present within an ontology by using descriptors. This example also highlights the use of the build() method.

    • Remove axioms from an ontology by using descriptors.

5. Some details about OWLOOP dependencies

Please use Gradle as the build tool for your project, and include the following dependencies in your project's build.gradle file:

  • aMOR (latest release is amor-2.2): a Multi-Ontology Reference library is based on OWL-API and it provides helper functions to OWLOOP.
    • OWL-API: a Java API for creating, manipulating and serialising OWL Ontologies. We have included owlapi-distribution-5.0.5 within amor-2.2.
  • OWLOOP (latest release is owloop-2.2): an API that enables easy manipulation of OWL (Ontology Web Language) ontologies from within an OOP (Object Oriented Programming) paradigm.
    • Pellet: an open source OWL 2 DL reasoner. We have included openllet-owlapi-2.5.1 within owloop-2.2.

6. Developers' message

Feel free to contribute to OWLOOP by sharing your thoughts and ideas, raising issues (if found) and providing bug-fixes. For any information or support, please do not hesitate to contact us through this Github repository or by email.

Developed by [email protected] and [email protected] under the supervision of [email protected].

7. License

OWLOOP is under the license: GNU General Public License v3.0

You might also like...
Implemented fully documented Particle Swarm Optimization algorithm (basic model with few advanced features) using Python programming language
Implemented fully documented Particle Swarm Optimization algorithm (basic model with few advanced features) using Python programming language

Implemented fully documented Particle Swarm Optimization (PSO) algorithm in Python which includes a basic model along with few advanced features such as updating inertia weight, cognitive, social learning coefficients and maximum velocity of the particle.

A programming language written with python
A programming language written with python

Kaoft A programming language written with python How to use A simple Hello World: c="Hello World" c Output: "Hello World" Operators: a=12

A general-purpose programming language, focused on simplicity, safety and stability.
A general-purpose programming language, focused on simplicity, safety and stability.

The Rivet programming language A general-purpose programming language, focused on simplicity, safety and stability. Rivet's goal is to be a very power

Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)
Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)

Machine Learning This project provides a web-interface, as well as a programmatic-api for various machine learning algorithms. Supported algorithms: S

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework
MazeRL is an application oriented Deep Reinforcement Learning (RL) framework

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems. Our vision is to cover the complete development life cycle of RL applications ranging from simulation engineering up to agent development, training and deployment.

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

Data & Code for ACCENTOR Adding Chit-Chat to Enhance Task-Oriented Dialogues

ACCENTOR: Adding Chit-Chat to Enhance Task-Oriented Dialogues Overview ACCENTOR consists of the human-annotated chit-chat additions to the 23.8K dialo

Official repository for
Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

Action-Based Conversations Dataset (ABCD) This respository contains the code and data for ABCD (Chen et al., 2021) Introduction Whereas existing goal-

Releases(2.1)
Owner
TheEngineRoom-UniGe
Human Robot Interaction and Artificial Intelligence Lab in Genoa, Italy.
TheEngineRoom-UniGe
Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices, ACM Multimedia 2021

Codes for ECBSR Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices Xindong Zhang, Hui Zeng, Lei Zhang ACM Multimedia 202

xindong zhang 236 Dec 26, 2022
A rule learning algorithm for the deduction of syndrome definitions from time series data.

README This project provides a rule learning algorithm for the deduction of syndrome definitions from time series data. Large parts of the algorithm a

0 Sep 24, 2021
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning Pixel-level Semantic Affinity with Image-level Supervision This code is deprecated. Please see https://github.com/jiwoon-ahn/irn instead. Int

Jiwoon Ahn 337 Dec 15, 2022
ECLARE: Extreme Classification with Label Graph Correlations

ECLARE ECLARE: Extreme Classification with Label Graph Correlations @InProceedings{Mittal21b, author = "Mittal, A. and Sachdeva, N. and Agrawal

Extreme Classification 35 Nov 06, 2022
Hack Camera, Microphone, Location, Clipboard With Just a Link. Also, Get Many Details About Victim's Device. And So On...

An Automated Tool to Hack Victim's Camera, Microphone, Location, Clipboard. Has 2 Extra Features. Version 1.1 Update Fixed Some Major Bugs Data Saving

ToxicNoob 36 Jan 07, 2023
VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning

VisualGPT Our Paper VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning Main Architecture of Our VisualGPT Downloa

Vision CAIR Research Group, KAUST 140 Dec 28, 2022
My published benchmark for a Kaggle Simulations Competition

Lux AI Working Title Bot Please refer to the Kaggle notebook for the comment section. The comment section contains my explanation on my code structure

Tong Hui Kang 29 Aug 22, 2022
[NeurIPS 2020] Official repository for the project "Listening to Sound of Silence for Speech Denoising"

Listening to Sounds of Silence for Speech Denoising Introduction This is the repository of the "Listening to Sounds of Silence for Speech Denoising" p

Henry Xu 40 Dec 20, 2022
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers

Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers Results results on COCO val Backbone Method Lr Schd PQ Config Download

155 Dec 20, 2022
An inofficial PyTorch implementation of PREDATOR based on KPConv.

PREDATOR: Registration of 3D Point Clouds with Low Overlap An inofficial PyTorch implementation of PREDATOR based on KPConv. The code has been tested

ZhuLifa 14 Aug 03, 2022
DNA-RECON { Automatic Web Reconnaissance Tool }

ABOUT TOOL : DNA-RECON is an automatic web reconnaissance tool written in python. This tool made for reconnaissance and information gathering with an

NIKUNJ BHATT 25 Aug 11, 2021
Few-Shot Graph Learning for Molecular Property Prediction

Few-shot Graph Learning for Molecular Property Prediction Introduction This is the source code and dataset for the following paper: Few-shot Graph Lea

Zhichun Guo 94 Dec 12, 2022
Source code and data from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits" by W. Bendada, G. Salha and T. Bontempelli

Carousel Personalization in Music Streaming Apps with Contextual Bandits - RecSys 2020 This repository provides Python code and data to reproduce expe

Deezer 48 Jan 02, 2023
PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

Yulun Zhang 1.2k Dec 26, 2022
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Trevor Ablett*, Bryan Chan*,

STARS Laboratory 8 Sep 14, 2022
Python program that works as a contact list

Lista de Contatos Programa em Python que funciona como uma lista de contatos. Features Adicionar novo contato Remover contato Atualizar contato Pesqui

Victor B. Lino 3 Dec 16, 2021
StarGAN v2-Tensorflow - Simple Tensorflow implementation of StarGAN v2

Official Tensorflow implementation Open ! - Clova AI StarGAN v2 — Un-official TensorFlow Implementation [Paper] [Pytorch] : Diverse Image Synthesis f

Junho Kim 110 Jul 02, 2022
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

Mehdi Cherti 135 Dec 30, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022