Practical Knowledge Modelling: Ontology Development 101
Capture machine-interpretable knowledge through ontology and semantic techniques
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What You Will Learn!
- Become better at approaching the organisation of information and knowledge in such a way that it makes sense to users
- Apply a methodology for developing seamless knowledge models (ontologies) and use that understanding across any subject matter
- Gain awareness of the inner workings of knowledge models (ontologies) expressed as visual and machine-interpretable representations
- Develop semantically-rich ontologies and knowledge graphs, formalized in the Web Ontology Language (OWL), using the Protégé ontology editor
Description
Ever wondered how you could capture and represent knowledge to share it with someone else, using the most efficient way possible? Are you interested in learning how knowledge can be pieced together for human interpretation and Artificial Intelligence?
Chances are we've probably all at some point been faced with situations where we wished there was a quicker, more effective, way of capturing and representing knowledge so that it makes sense to human beings and computers. Knowledge modelling (or technically speaking, ontology modelling) is about the tools and techniques for capturing and representing knowledge. A knowledge model (a.k.a. ontology) is, basically, a representation that provides a basis for sharing meaning about some subject matter.
There are a great many uses of knowledge modelling from Artificial Intelligence to the Semantic Web, natural language processing, augmented controlled vocabularies & thesauri, reference models used in business analysis, engineering and heaps more. In this course, you'll learn how to go about modelling knowledge from a practical perspective, which means that in addition to getting an appreciation of the context of knowledge modelling, you'll also be expected to get your hands dirty! So, we'll be looking at applying different methods for building knowledge models. These methods include graphical as well as formal computer-aided techniques.
This course is for people who care about knowledge sharing and making knowledge a true asset to support knowledge management, systems interoperability, intelligent information architecture, best practice knowledge capture, and many more.
Who Should Attend!
- Data-oriented professionals with an interest in machine-interpretable methods for knowledge capture and sharing
- Individuals who operate in areas like information and knowledge management, business analysis, enterprise architecture, information systems, etc.
- Professionals intending to work with Semantic Web-based knowledge graph technologies and graph databases
