The current generation has seen technology penetrate every aspect of our life. However, even with recent advancements, adopters of contemporary technology are often angry and frustrated with their devices. With the increasing number of devices available to us in our day-to-day lives, and with the emergence of newer technologies like the Internet of Things, there is a stronger need than ever for computers to better understand human needs. However, there is still no machine understandable vocabulary that conceptualizes and describes the human-needs domain. As such, in this thesis we present a cloud-based ontology solution that conceptualizes the needs-domain by describing the relationships between the concepts of an Agent, a Role, a Need, and a Satisfier.
The thesis focusses on the design of an OWL ontology which is based on an existing human-needs model. The human-needs model chosen for the ontology stems from a trans-disciplinary approach led by Manfred Max-Neef, called the Fundamental Human Needs model. It is seen as classifiable, finite and constant across all cultures and time periods. The methodology approach used to develop a new ontology is METHONTOLOGY, which is geared toward conceptualizing an ontology from scratch with the mindset of continual evaluation.
We then further discuss the overall FHN Ontology comprising of various components including a RESTful Web Service and a SPARQL endpoint for querying and updating the FHN Ontology. The ontology is evaluated via competency questions for validation and via the Ontology Pitfall Scanner for verification and correctness across multiple criteria. The entire system is tested and evaluated by implementing a native android application which serves as a REST client to connect to the FHN Ontology end-point
Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/33183 |
Date | January 2015 |
Creators | Dsouza, Shawn Dexter |
Contributors | El Saddik, Abdulmotaleb |
Publisher | Université d'Ottawa / University of Ottawa |
Source Sets | Université d’Ottawa |
Language | English |
Detected Language | English |
Type | Thesis |
Page generated in 0.0023 seconds