Capture machine-interpretable knowledge through ontology and semantic techniques
What you'll 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
Requirements
Diagramming tool for drawing shapes, e.g. Microsoft Office Visio, yEd, Diagrams .net, Lucidchart, UML design tools, etc., or simply pen and paper
Spreadsheet application, e.g. Microsoft Office Excel or similar
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 this course is for
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
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