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.
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Initial created on
November 2, 2023.
For additional information, contact
Tumelo Modise (tumelomodise4@gmail.com).
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Notes
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
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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
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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.
