tumelomodise/LFTDO

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. # LFTDO - Livestock Farming Theft Detection Ontology )-1F4E79) --- ## Overview 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. > **Research Context:** Master of Computer Science dissertation - Tshwane University of Technology (TUT). > **Researcher:** Tumelo Modise > **Supervisors:** Dr. A Buitendag > **Co-Supervisors:** Prof JC Jansen van Vuuren and Dr. Z Dawood > **Namespace:** `github.com` --- ## Repository Structure ``` LFTDO/ ├── README.md ← This file — repository overview ├── LICENSE ← CC BY 4.0 ├── v0.04/ │ ├── README.md ← v0.04 release notes and metrics │ ├── LFTDO_v0.04.owl ← Initial validated OWL file │ └── ABox/ │ └── Scenario1_2/ ← Manual Protégé ABox (Scenarios 1 & 2) ├── v0.05/ │ ├── README.md ← v0.05 release notes and metrics │ ├── LFTDO_v0.05.owl ← Revised OWL file │ └── ABox/ │ └── Scenario1_2/ ├── ... ├── v0.12/ │ ├── README.md ← v0.12 current release │ ├── LFTDO_v0.12.owl ← Final production OWL file │ ├── ABox/ │ │ ├── Cellfie/ │ │ │ ├── 1_Sheep_SensorData_LoadData.xls │ │ │ ├── 1_SensorData_MAPPING_RULE.json │ │ │ └── [additional entity file pairs] │ │ └── Scen …