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
Machine learning algorithms for many-body quantum systems

NetKet NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and

NetKet 413 Dec 31, 2022
Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR

Codebase for "INVASE: Instance-wise Variable Selection" Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar Paper: Jinsung Yoon, James Jordon,

Jinsung Yoon 50 Nov 11, 2022
Here I will explain the flow to deploy your custom deep learning models on Ultra96V2.

Xilinx_Vitis_AI This repo will help you to Deploy your Deep Learning Model on Ultra96v2 Board. Prerequisites Vitis Core Development Kit 2019.2 This co

Amin Mamandipoor 1 Feb 08, 2022
This repository contains the reference implementation for our proposed Convolutional CRFs.

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

Marvin Teichmann 553 Dec 07, 2022
Code for paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Decoupled Spatial-Temporal Graph Neural Networks Code for our paper: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

S22 43 Jan 04, 2023
This code is 3d-CNN model that can predict environmental value

Predict-environmental-value-3dCNN This code is 3d-CNN model that can predict environmental value. Firstly, I built a model that can create a lot of bu

1 Jan 06, 2022
Breast Cancer Classification Model is applied on a different dataset

Breast Cancer Classification Model is applied on a different dataset

1 Feb 04, 2022
ICS 4u HD project, start before-wards. A curtain shooting game using python.

Touhou-Star-Salvation HDCH ICS 4u HD project, start before-wards. A curtain shooting game using python and pygame. By Jason Li For arts and gameplay,

15 Dec 22, 2022
The official homepage of the COCO-Stuff dataset.

The COCO-Stuff dataset Holger Caesar, Jasper Uijlings, Vittorio Ferrari Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augment

Holger Caesar 715 Dec 31, 2022
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

⚠️ ‎‎‎ A more recent and actively-maintained version of this code is available in ivadomed Stacked Hourglass Network with a Multi-level Attention Mech

Reza Azad 14 Oct 24, 2022
Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks.

[TensorFlow] Protein Interface Prediction using Graph Convolutional Networks Unofficial TensorFlow implementation of Protein Interface Prediction usin

YeongHyeon Park 9 Oct 25, 2022
Code for Ditto: Building Digital Twins of Articulated Objects from Interaction

Ditto: Building Digital Twins of Articulated Objects from Interaction Zhenyu Jiang, Cheng-Chun Hsu, Yuke Zhu CVPR 2022, Oral Project | arxiv News 2022

UT Robot Perception and Learning Lab 78 Dec 22, 2022
Official repository for the paper "Self-Supervised Models are Continual Learners" (CVPR 2022)

Self-Supervised Models are Continual Learners This is the official repository for the paper: Self-Supervised Models are Continual Learners Enrico Fini

Enrico Fini 73 Dec 18, 2022
A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

Benedek Rozemberczki 259 Dec 05, 2022
Code for STFT Transformer used in BirdCLEF 2021 competition.

STFT_Transformer Code for STFT Transformer used in BirdCLEF 2021 competition. The STFT Transformer is a new way to use Transformers similar to Vision

Jean-François Puget 69 Sep 29, 2022
A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

Yutian Liu 2 Jan 29, 2022
Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

picinpics Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of

RodrigoCMoraes 1 Oct 24, 2021
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Zhuang Liu 4.5k Jan 03, 2023
Alex Pashevich 62 Dec 24, 2022
Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads-Tutorial-3 Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads Inc 2 Jan 03, 2022