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
Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u

Seattle university Renewable energy research 7 Sep 26, 2022
Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Blacksmith Rowhammer Fuzzer This repository provides the code accompanying the paper Blacksmith: Scalable Rowhammering in the Frequency Domain that is

Computer Security Group @ ETH Zurich 173 Nov 16, 2022
An open source app to help calm you down when needed.

By: Seanpm2001, Et; Al. Top README.md Read this article in a different language Sorted by: A-Z Sorting options unavailable ( af Afrikaans Afrikaans |

Sean P. Myrick V19.1.7.2 2 Oct 24, 2022
✔️ Visual, reactive testing library for Julia. Time machine included.

PlutoTest.jl (alpha release) Visual, reactive testing library for Julia A macro @test that you can use to verify your code's correctness. But instead

Pluto 68 Dec 20, 2022
[NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Code for NeurIPS 2021 paper "Better Safe Than Sorry: Preventing Delu

Lue Tao 29 Sep 20, 2022
[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"

Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks By Yikai Wang, Yi Yang, Fuchun Sun, Anbang Yao. This is the pytorc

Yikai Wang 26 Nov 20, 2022
The final project of "Applying AI to 3D Medical Imaging Data" from "AI for Healthcare" nanodegree - Udacity.

Quantifying Hippocampus Volume for Alzheimer's Progression Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that result

Omar Laham 1 Jan 14, 2022
Exploration & Research into cross-domain MEV. Initial focus on ETH/POLYGON.

xMEV, an apt exploration This is a small exploration on the xMEV opportunities between Polygon and Ethereum. It's a data analysis exercise on a few pa

odyslam.eth 7 Oct 18, 2022
Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering

Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering Modular Primitives for High-Performance Differentiable Rendering Samuli

NVIDIA Research Projects 675 Jan 06, 2023
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
Wileless-PDGNet Implementation

Wileless-PDGNet Implementation This repo is related to the following paper: Boning Li, Ananthram Swami, and Santiago Segarra, "Power allocation for wi

6 Oct 04, 2022
Aydin is a user-friendly, feature-rich, and fast image denoising tool

Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.

Royer Lab 99 Dec 14, 2022
Tensorflow Implementation of ECCV'18 paper: Multimodal Human Motion Synthesis

MT-VAE for Multimodal Human Motion Synthesis This is the code for ECCV 2018 paper MT-VAE: Learning Motion Transformations to Generate Multimodal Human

Xinchen Yan 36 Oct 02, 2022
Website for D2C paper

D2C This is the repository that contains source code for the D2C Website. If you find D2C useful for your work please cite: @article{sinha2021d2c au

1 Oct 21, 2021
DAT4 - General Assembly's Data Science course in Washington, DC

DAT4 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (12/15/14 - 3/16/15). Instructors: Sinan Ozdemir

Kevin Markham 779 Dec 25, 2022
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

DASR Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021 [arXiv] Overview Requirements

Longguang Wang 318 Dec 24, 2022
Vignette is a face tracking software for characters using osu!framework.

Vignette is a face tracking software for characters using osu!framework. Unlike most solutions, Vignette is: Made with osu!framework, the game framewo

Vignette 412 Dec 28, 2022
🛰️ Awesome Satellite Imagery Datasets

Awesome Satellite Imagery Datasets List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datase

Christoph Rieke 3k Jan 03, 2023
RL and distillation in CARLA using a factorized world model

World on Rails Learning to drive from a world on rails Dian Chen, Vladlen Koltun, Philipp Krähenbühl, arXiv techical report (arXiv 2105.00636) This re

Dian Chen 131 Dec 16, 2022