URI*
|
https://github.com/tumelomodise/LFTDO
https://github.com/tumelomodise/LFTDO
|
Abstract
|
Livestock Farming Theft Detection Ontology(LFTDO) is a formal Livestock Farming ontology. The focus of the ontology will be on the agriculture domain for Livestock Theft Detection concepts.
The purpose of constructing this ontology is to provide a cognitive knowledge-based model for the Livestock Farming domain to be used by the farmers and veterinaries with a goal of Improving Livestock Farming Management to reduce livestock theft, and reducing cost. The ontology should be able to reason on the Livestock Behaviours to detect the abnormally provided by the devices fitted on the livestock with sensor to infer potential theft to farmer.
The purpose of Livestock Farming Theft Detection Ontology(LFTDO) is to integrate and analyze data from various sources, such as sensor networks, surveillance cameras, and databases, to identify suspicious activities that may indicate theft in a farming area. Furthermore, it enables livestock theft detection and prevention systems and applications to be accessible, flexible, transparent, interoperable, scalable, and cost effective.
The aim is to assist with the early detection of livestock theft by providing a structured and standardized way of representing knowledge about livestock farming, including:
1.Types of livestock
2.The vital components of livestock farming include ownership, farming areas, grazing areas, water sources, and technological resources (i.e. IoT devices, gateways, cloud computing, and machine-learning algorithms).
3.A variety of activities happening such as livestock movement, grazing, and feeding,
4.The people involved in livestock farming include farmers, extended-farmer officers, law enforcement agencies, and veterinarians.
5.A variety of events occurs in a farming area such as livestock theft
LFTDO standardizes the data structures, integration, and knowledge representation of livestock farming by defining the concepts, entities, relationships, and rules governing them. The ontology should be able to reason livestock behaviour patterns and environmental conditions data provided to infer the possibility of livestock theft to alert livestock farmers and other stakeholders.
See more...
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Bug database
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https://github.com/tumelomodise/LFTDO/issues
https://github.com/tumelomodise/LFTDO/issues
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Competency question
|
https://github.com/tumelomodise/LFTDO/tree/main/v0.12/SPARQL
See more...
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Contact*
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Tumelo Modise (tumelomodise4@gmail.com)
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Rights holder
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|
Object creation date property
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http://purl.org/dc/elements/1.1/created
http://purl.org/dc/elements/1.1/created
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Submission date
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June 2, 2026
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Depiction
|
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_Architecture_%28Onto_AIoTA%29_Diagram.png
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_Architecture_%28Onto_AIoTA%29_Diagram.png
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_Conceptual_Diagram.png
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_Conceptual_Diagram.png
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_OntoGraf_Visualisation.png
https://raw.githubusercontent.com/tumelomodise/LFTDO/main/docs/LFTDO_OntoGraf_Visualisation.png
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Deprecated
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false
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Description*
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The Livestock Farming Theft Detection Ontology (LFTDO) is a domain-specific OWL 2 DL ontology designed to support intelligent, semantically-driven livestock theft detection in rural South Africa. It provides a formal knowledge representation of livestock behaviour, IoT sensor data, geofence monitoring, and theft event classification enabling AI-driven reasoning over real-time sensor streams from GPS tracking collars and IoT devices.
The LFTDO is the knowledge core of the Onto-AIoTA (Ontology-based IoT AI Architecture), where it functions as the Semantic Layer transforming raw sensor data into classified, severity-graded theft alerts through 27 SWRL inference rules.
See more...
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Documentation
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https://github.com/tumelomodise/LFTDO/
https://github.com/tumelomodise/LFTDO/
|
Funded or sponsored by
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|
Contributor
|
|
Creator
|
|
Subject
|
http://aims.fao.org/aos/agrovoc/c_2807
See more...
http://aims.fao.org/aos/agrovoc/c_4397
See more...
http://aims.fao.org/aos/agrovoc/c_32522
See more...
http://aims.fao.org/aos/agrovoc/c_a97eb278
See more...
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Formality level
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http://w3id.org/nkos/nkostype#ontology
http://w3id.org/nkos/nkostype#ontology
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License
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Representation language*
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OWL
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Syntax
|
http://www.w3.org/ns/formats/OWL_Manchester
http://www.w3.org/ns/formats/OWL_Manchester
|
Homepage
|
https://github.com/tumelomodise/LFTDO/
https://github.com/tumelomodise/LFTDO/
|
Indexed or included in catalog or repository
|
https://data.agroportal.eu/
https://data.agroportal.eu/
https://stage.agroportal.eu
stage.agroportal.eu
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Generic type
|
http://omv.ontoware.org/2005/05/ontology#DomainOntology
http://omv.ontoware.org/2005/05/ontology#DomainOntology
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Key classes
|
https://github.com/tumelomodise/LFTDO#TheftEvent
See more...
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Known usage
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Reasons over livestock behaviour and environmental conditions to infer theft risks and alert farmers and relevant stakeholders.
See more...
Provides a standardized representation of livestock, ownership, farming and grazing areas, water sources, people, activities, technology, and theft events.
See more...
Integrates sensor, surveillance, and database data to identify suspicious activity and support early livestock-theft detection.
See more...
Enables flexible, transparent, interoperable, scalable, and cost-effective theft-detection and prevention applications.
See more...
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Metadata vocabulary used
|
http://www.w3.org/2002/07/owl#
http://www.w3.org/2002/07/owl#
http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/2000/01/rdf-schema#
http://purl.org/dc/terms/
http://purl.org/dc/terms/
|
Modification date
|
March 1, 2026
|
Object modification date property
|
http://purl.org/dc/elements/1.1/modified
http://purl.org/dc/elements/1.1/modified
|
Natural language
|
|
Notes
|
Developed in the context of Masters of Computing in Computer Science
See more...
Livestock Farming Theft Detection Ontology(LFTDO)
Author: Tumelo Modise, 209332175,Date : 13 September 2023,
Updated: 01/03/2026
University: Tshwane University of Technology(TUT)
Subject:Masters of Computing in Computer Science
Supervisors: Dr A Buitengdag,Dr Z Dawood,Prof J
Introduction
-----------------------------------------------------------------------------------------------------------
Livestock Farming Theft Detection Ontology(LFTDO) is a formal Livestock Farming ontology. The focus of the ontology will be on the agriculture domain for Livestock Theft Detection concepts.
The purpose of constructing this ontology is to provide a cognitive knowledge-based model for the Livestock Farming domain to be used by the farmers and veterinaries with a goal of Improving Livestock Farming Management to reduce livestock theft, and reducing cost. The ontology should be able to reason on the Livestock Behaviours to detect the abnormally provided by the devices fitted on the livestock with sensor to infer potential theft to farmer.
Purpose
-----------------------------------------------------------------------------------------------------------
The purpose of Livestock Farming Theft Detection Ontology(LFTDO) is to integrate and analyze data from various sources, such as sensor networks, surveillance cameras, and databases, to identify suspicious activities that may indicate theft in a farming area. Furthermore, it enables livestock theft detection and prevention systems and applications to be accessible, flexible, transparent, interoperable, scalable, and cost effective.
The aim is to assist with the early detection of livestock theft by providing a structured and standardized way of representing knowledge about livestock farming, including:
1.Types of livestock
2.The vital components of livestock farming include ownership, farming areas, grazing areas, water sources, and technological resources (i.e. IoT devices, gateways, cloud computing, and machine-learning algorithms).
3.A variety of activities happening such as livestock movement, grazing, and feeding,
4.The people involved in livestock farming include farmers, extended-farmer officers, law enforcement agencies, and veterinarians.
5.A variety of events occurs in a farming area such as livestock theft
LFTDO standardizes the data structures, integration, and knowledge representation of livestock farming by defining the concepts, entities, relationships, and rules governing them. The ontology should be able to reason livestock behaviour patterns and environmental conditions data provided to infer the possibility of livestock theft to alert livestock farmers and other stakeholders.
See more...
|
Preferred namespace prefix
|
lftdo
See more...
|
Preferred namespace URI
|
https://github.com/tumelomodise/LFTDO#
https://github.com/tumelomodise/LFTDO#
|
Publisher
|
|
Creation date*
|
November 2, 2023
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Repository
|
https://github.com/tumelomodise/LFTDO
https://github.com/tumelomodise/LFTDO
|
Status*
|
alpha
See more...
|
URI Lookup Endpoint
|
https://data.agroportal.eu/search?ontologies=LFTDO&require_exact_match=true&q=
https://data.agroportal.eu/search?ontologies=LFTDO&require_exact_match=true&q=
|
Identifier pattern
|
https://github.com/tumelomodise/LFTDO#
See more...
|
Created with
|
Protégé
See more...
|
Version information
|
12
See more...
|
Version IRI
|
https://github.com/tumelomodise/LFTDO/v0.12
https://github.com/tumelomodise/LFTDO/v0.12
|
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