Ontology

New capabilities involving sensors, data collection, and data analysis have enabled innovations in how engineered systems are monitored and maintained. Whereas each new evolution of maintenance philosophies has relied upon the current technological state, this research examines potential future capabilities in the field of prognostics and health management (PHM). PHM algorithms for predicting the estimated time to failure for a system are based on sensor data, physical models, or a combination of both.

This dataset describes ontologies relevant for the domain of systems engineering and electronics. We developed various .owl ontologies that describe the domain and collected related ontologies that are relevant. This includes: hardware like processors, sensors, actuators, processor parts, adcs, software, systems like embedded systems, automobiles and driving assistance systems. Further their properties and functions can be described. The base ontology is the GENIAL! Basic Ontology (GBO) which is based on BFO (GENIALOntBFO.owl).

In recent years, teaching-learning methods have emerged into a completely new dimension from what used to be a traditional approach. The in-person lectures have been converted into online virtual learning, the traditional record-keeping has been replaced by robust learning management systems which have made the teaching-learning process lot more efficient and convenient. However, the increased use of digital storage and access to students' personal information raises the concern about data privacy and security for both LET (Learning, Education and Training) users and providers.

TreatO is a rice disease control ontology that focuses on modeling biological control agents and chemical controls agent of each rice pest (e.g., rice diseases and insects).

RiceDO is a rice disease ontology that focuses on modeling abnormal appearances of a rice plant when damaged by pests (e.g., rice diseases and insects).

This is an ontology used for the identification of common algae involved in harmful algal bloom events. This ontology is used as a guide to determine the algae species to be identified by the expert system.

The ASU/UNSW-CICMOD01 was developed to support the novel Cyber Influence Campaign (CIC) model and ontology. It contains full captures of specific tags (hashtags) regardling individual cyber influence campaings scrapped from Twitter and Instagram.  

The compressed file contains:

• Data files in spreadsheet format from three different networks (friendship, companionship and acquaintances).
• Analysis files from UCINET, Pajek, Cytoscape and Gephi.

It is thus possible to corroborate the results mentioned in different studies that refer to these data.

OntoSNAQA is the name that combines Social Network Analysis (SNA), People and Questionnaires (Question and Answers - QA).

This ontology will be updated in this project of github and in the url http://www.jabenitez.com/ontologies/OntoSNAQA.owl.

It's an ontology that combines three different domains:
- People
- Questionnaires
- Social Network Analysis terms